inflation case study real estate worksheet

Excel: Financial Modelling for a Real Estate Investment

Note: if you are looking for a freelancer to build a custom financial model for your business, I'm available. Learn more here .

In our previous tutorial, we learned the concept of problem-solving modelling . Today, we're stepping into a more advanced realm, financial modelling in Excel, with a real-life example.

Before we start, you can download the Excel model by clicking this link . It contains 3 tabs: Inputs , Calculations , and Outputs , that we will cover below.

The Problem

You're interested in investing in real estate: buying an apartment and renting it out. Is it a profitable investment? How much do you need to invest? Real estate investment is exciting, but it can be tricky to navigate the financial aspects.

To make an informed decision, you want to forecast your cash flows and understand your potential returns. The financial model we're introducing here will help you do just that. Let's see how it works!

We start by defining our investment assumptions in the Inputs tab. Here is the data you either have at hand or can make educated guesses about.

For our scenario, the main inputs you need to set are:

• Property Purchase Date and Price.
• Down Payment: the upfront payment made when buying the property.
• Mortgage Rate: the interest rate on the loan.
• Mortgage Duration: the duration of the loan in years.
• Monthly Rent: the amount you plan to charge tenants each month.
• Occupation Rate: the percentage of time your property is rented out over a year.
• Expenses: regular costs that you need to pay like maintenance, property taxes, insurance, etc.
• Rental Income Tax Rate: the percentage of gross rental income that is paid to the government as income tax.
• Tax Deductible Expenses: costs that can be subtracted from the rental income, thus reducing the overall tax.
• Property Sell Date: the date when you sell your property.
• Appreciation Rate: the yearly rate at which the property’s value increases.
• Inflation Rate: the percentage increase of prices for goods and services in our economy over a year.

Fill in the inputs based on your specific situation or projections. The better your estimates, the more reliable the results.

So, how do we turn these inputs into meaningful information?

Intermediary Calculations

Now, let's move onto the Calculations tab, which is essentially the powerhouse of our model.

The entire tab is automatically calculated and updated based on the assumptions you provided. The goal of this tab is to break down complex computations into intermediary steps.

There are different computation modules, each dedicated to calculating a specific aspect of the real estate investment, such as revenues, operating expenses, mortgage, tax, and more. You can find the the intermediary results at the top of the tab.

If you are curious about the magic that makes these calculations work, we're crafting a series of tutorials that will open up each module for you.

The Outputs

The Outputs tab is possibly the most sought-after section of our financial model. It contains a dashboard revealing the details of your real estate investment performance.

Here you can explore four key aspects of your investment:

• Uses and Sources for Property Purchase: this section breaks down the costs of acquiring the property and your funding strategy.
• Investment Return Indicators: this presents critical indicators to measure your investment's profitability.
• Mortgage Info: if you've opted for a bank loan to finance your purchase, this area holds all relevant details.
• Cash Flow Chart: this dynamic chart visualizes the year-on-year cash flow scenario of your property. You can choose to display either annual cash flow or monthly average cash flow per year.

This dashboard is customizable, look out for our upcoming tutorials with tips on how to personalize it.

Exploring Different Scenarios

By adjusting the inputs, you can simulate different scenarios and see how they affect your potential returns. This is a powerful way to plan and prepare for a variety of outcomes.

Once you've entered the inputs, the calculations and outputs update automatically, allowing you to easily adjust and test different scenarios.

Model Limitations

Before we conclude, it's important to be aware of the limitations of this model:

• One investment per simulation: this model assumes you're buying a single property. If you have a portfolio of investments, you might need to duplicate the model or adapt it to suit your needs.
• Annual calculation: this model calculates returns on an annual basis. This is a simplification that can lead to less precise results, but it still gives you a good high-level overview.
• High-level simulation: this model includes only essential assumptions and some calculations are simplified, like income tax. If your investment scenario includes unique factors not covered in this model, you may need to modify it.

By breaking down the problem into inputs, intermediary calculations, and outputs, real estate investment analysis becomes far more manageable. With the help of this model, you can confidently navigate your real estate investment journey.

Don't forget to download the model for yourself and start exploring. Stay tuned, keep learning, and happy investing!

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• How and when to use the four COUNT functions
• Understand IF, nested IF, and IFS functions
• Three features to handle a spreadsheet full of data

See all the tutorials on the homepage

A Framework For Assessing the Impact of Inflation on Real Estate

June 28, 2022.

Featured in PREA Quarterly Spring 2022 issue:

“when full employment is reached, any attempt to increase investment still further will set up a tendency in money-prices to rise without limit…i.e. we shall have reached a state of true inflation.” — john maynard keynes[1].

With an unemployment rate of 3.6%, year-over-year job openings growing at 7.1%, and average monthly nonfarm payroll gains exceeding 500,000 in the past year, the US economy arguably has reached a state of Keynesian full employment and true inflation. Signs of inflation are pervasive in year-over-year statistics: the headline Consumer Price Index (CPI) rose by 8.3% in April, the GDP deflator increased 8.0% in 1Q22, and West Texas Intermediate crude oil prices breached $100 per barrel earlier this year for the first time since 2014. Real estate costs have not been invulnerable to these macroeconomic shifts. According to one prominent index, construction costs increased by 7.1% year-over-year as of 1Q22.[2] Additionally, the Bureau of Labor Statistics reported that total compensation for individuals in the real estate, rental, and leasing sector increased by 5.2% in 1Q22 relative to 1Q21.

Conditions not seen since the early 1980s now confront real estate investors, who want to know how higher inflation impacts cash flows, valuations, and returns. The preponderance of empirical studies Heitman has reviewed on private real estate and inflation shows that real estate has been at least a partial inflation hedge. However, incorporating that conclusion into a broader risk, return, and asset allocation framework adds another layer of complexity. Uncertainty abounds in the current environment with respect to the direction of the economy, inflation, and real estate returns, and investors need a method that incorporates both their informed views and the dispersion surrounding their assumptions. Heitman has developed a framework to address these issues.

Heitman views the contour of macroeconomic consensus forecast distributions as a useful starting point for thinking about market expectations. Further, real estate market participants can use these distributions—with proprietary adjustments—in a forward-looking simulation analysis to understand more thoroughly inflation risks and their impact on real estate.

Real Estate as a Partial Inflation Hedge

When inflation was raging in the late 1970s, Eugene Fama and William Schwert set out to determine the extent to which stocks, bonds, and real estate were inflation hedges.[3] In the pre-NCREIF era, the authors relied on the housing component of the CPI to conduct tests for residential real estate and found that it was a “complete” inflation hedge (i.e., it served as a hedge against both expected and unexpected inflation). New data and econometric techniques have flourished over the past 45 years since the article was written, but its methods are still applicable.

In that 1977 paper, Fama and Schwert disaggregated inflation into expected and unexpected components, drawing inspiration from Irving Fisher’s famous equation: nominal interest rate = expected real return + expected inflation rate. Empirically, the proxy for expected inflation was the Treasury bill yield, and the stand-in for unexpected inflation was the inflation rate minus the Treasury bill yield. If the regression coefficient for expected inflation and that for unexpected inflation were indistinguishable from 1.0, then Fama and Schwert deemed the asset to be a “complete hedge against inflation.”

Taking inspiration from these assertions, Heitman reproduced a fragment of the study using NCREIF data and slightly different methods. [4] Exhibit 1 stacks NCREIF Property Index (NPI) return sensitivities to expected and unexpected inflation on a rolling, trailing five-year basis over the past few decades. The exhibit highlights that real estate is a partial inflation hedge, on average, and that its inflation hedging abilities are time varying. It may be that the results from this chart (and many modern academic studies) reach a more modest conclusion relative to that of Fama and Schwert (real estate as a partial instead of a complete inflation hedge) in part because of the underlying indices used. [5] But it is important to note that real estate also seems to be a good hedge against inflation when inflation levels are high, although data is limited. Given estimation error and other limitations, investors should also consider commonsense or theoretical models to supplement these types of calculations. For example, the presence of explicit CPI provisions, percentage rent, and other features of leases and real estate itself should create at least some inflation protection. In addition, real estate investors may wish to incorporate these kinds of foundational assumptions into a broader risk, return, and asset allocation framework. The remainder of this article progresses toward a simplified illustration of one such synthesis.

A Non-Normal Environment for Inflation

Many economic and financial variables exhibit non-normal return distributions, and inflation is no exception. Since 1948, US CPI inflation on a year-over-year basis has hovered in the 0% to 5% range in just under 75% of months. That means more than 25% of the time, inflation might plausibly be characterized as uncomfortably high or low (outside the 0% to 5% range), as demonstrated in Exhibit 2. The chart shows heavy tails and a conspicuous long right tail of the distribution, which explains why mean inflation since 1948 is 3.5% while the median is only 2.8%. For comparison purposes, a simulated normal distribution with a mean of 2.8% is also included in the graph.

For purposes of forward-looking decision-making, perhaps just as interesting is the fact that macroeconomic consensus forecast distributions may exhibit unexpected shapes. Often, the configuration of consensus forecasts for GDP, inflation, unemployment, the federal funds rate, the ten-year note, and other variables are surprising. For example, given the long right tail in historical CPI percentage changes shown in the exhibit and the uncertainty about the path of future inflation, a nontrivial number of forecasts might be expected to differ significantly from the middle of the pack. But for 4Q22, inflation forecasts in the tails are not dramatically different from that implied by a normal distribution. In part, this phenomenon occurs because consensus forecast distributions represent measures of central tendency without variance around the forecasts. Even so, the amount of certainty rooted in consensus forecasts might be surprising to some market participants.

Why does the contour of consensus forecast distributions matter? Because the underlying assumptions represent the views of some of the largest financial institutions with extensive sway in markets. Rather than starting from scratch, real estate investors can use the distributions to begin a conversation about initial capital market expectations. For example, if a real estate investor believes that the consensus distribution for CPI does not sufficiently capture inflation risk, then the analyst might create macroeconomic suppositions with an inflation distribution that is positively skewed (long right tail) relative to that of consensus. Subsequently, the investor could take additional steps toward a broader framework.

Steps Toward a Quantitative Procedure

An approachable quantitative procedure could advance along these five steps:

• Gather macroeconomic consensus forecast distributions for the applicable variables and other relevant data.
• Fit the individual distributions along with any adjustments for a “house view.”[6]
• Specify how the variables are related to one another.
• Simulate all variables forward over the given horizon.
• Analyze risk, return, and portfolio construction.

Even though these steps entail some complexity, the procedure is approachable in the important sense that the analysis can be performed in a spreadsheet without the need for more advanced econometric software.[7]

Example: Inflation Risk and Real Estate Price Changes in a Dynamic Framework

Returning to the Keynes quote at the beginning of the article can help frame a model for purposes of illustration.[8] Namely, the much-discussed time-varying, short-term trade-off between the inflation rate and the unemployment rate is used as the basis for a rudimentary macroeconomic framework. Inflation and unemployment may be combined with changes in real estate prices to observe how the three variables evolve over time and influence one another. Specifically, the headline CPI, the unemployment rate, and commercial property prices are used and information over the historical period December 2000 to February 2022 is gathered.[9] Moreover, the macroeconomic consensus forecast distributions for the period 4Q21 to 4Q22 are analyzed because the analysis of a one-year period is straightforward. On their own, the mean and median for inflation and unemployment forecasts are fairly close to each other, suggesting somewhat symmetrical distributions.[10] In this example, the shape of the distribution for real estate is based on the historical contour of price changes, but PREA’s Consensus Forecast Survey can be used in future research.

A real estate investor either may adopt the general shape and central position of the forecast distribution or may adjust it. If a market participant has no special insight into inflation or unemployment forecasts, then the general form of the consensus distribution can be assumed. This option may be appropriate for many real estate observers who do not generate proprietary forecasts of macroeconomic variables. Because the forecast distributions represent individual estimates of the central outcome, they may understate the true dispersion in the forecast. Therefore, even if the shape of the forecast distribution is adopted, there is room to increase the weight in the tails to account for more risk.[11]

Some institutions have their own forecasts or prefer to weight specific forecasts more heavily (perhaps in a way that changes the configuration of the distribution). In such a case, the institution should use its own house view instead of embracing the more passive approach described. But investors should be mindful of overconfidence bias when doing so. For purposes of this example, the general shape of consensus forecast distributions is considered as a starting point, but based on the shape of the CPI historically and a hypothesis, just for illustration, that inflation surprises will be on the upside, subjective distributions are created by variable and by month that assign upside risk to inflation and the unemployment rate. The historical distribution for real estate prices is negatively skewed (long left tail), and this general shape is applied for the forecast.

Correlation analysis is one useful method for understanding the relationship among variables. But static correlation matrices should be used with caution for a variety of reasons.[12] For purposes of this example, a couple of basic adjustments are included to increase the likelihood that the correlation estimates are useful, but more sophisticated adjustments should be contemplated. Specifically, all three variables are normalized before calculating correlations, and correlations from different time periods are applied (50% probability of the correlation matrix from April 2020 to the present, 30% from 2011 to March 2020, and 20% from 2001 to 2010; only one correlation matrix is used for each simulation run over the one-year forecast horizon). Finally, each variable is related to its past values through a regression model.[13] In summary, real estate prices, inflation, and unemployment are related to their own past values as a baseline forecast; forecast distributions around this baseline are applied by variable and by month, as described in the “Gather” section; and the normalized variables are related to one another through correlation matrices (more recent correlations are given a higher weight).

Changes in real estate prices and inflation and differences in the unemployment rate are all simulated forward over a one-year period.[14] Importantly, correlated variables are simulated forward; the model does not use indices independent of one another. In other words, uncertainty is integrated into the framework but in such a way that allows for dynamic relationships among the variables.

The results convey that the average real estate price appreciation over the forecast horizon is 3.3%, CPI inflation is 7.5%, and the increase in the unemployment rate is 260 basis points (bps). Inflation and unemployment risk are on the upside, but the average and median for real estate are quite close (a more symmetrical distribution). If these results are to be believed, portfolios could be tilted to favor assets with shorter lease terms, downside protection, and related attributes. However, pricing is key in the portfolio construction process because savvy investors with a similar view may have already caused market pricing to reflect these valued attributes.

The most likely outcome is that the average of the simulated forecasts in Exhibit 4 will be wrong. But even though these forecasts are quite likely to be wrong, the method outlined can be used to appreciate inflation and real estate risk more thoroughly and identify other areas in which underappreciated risks should be acknowledged. Wrestling with the assumptions and mechanics of the model can be just as important or more important than observation of the forecasts themselves.

A discrete probability table is another way to display the results. One advantage of this approach is its concreteness and intuitiveness that may reinforce insights from the continuous probability distributions. The views of an investor and the set of possible outcomes are captured holistically in the continuous distributions, but discrete scenarios may be useful for purposes of communication. Exhibit 5 shows one way of presenting the information, but the number of scenarios and other features can be adjusted based on personal preferences. In the table, for Scenario #2, a 50% probability is assigned to the median values derived from the continuous distributions, and the remaining values are implied based on the probabilities given and the relationships described previously. Scenario #1 shows lower inflation but at the cost of higher unemployment and no real estate price appreciation. Conversely, Scenario #3 depicts higher inflation, continued low unemployment, and higher real estate price appreciation.

The contour of macroeconomic consensus forecast distributions can be combined with insights from historical data along with proprietary assumptions and integrated into a dynamic framework. This framework may be used to assess the impact of inflation on real estate more thoroughly and to consider how portfolio construction might need to adapt to the current environment. It is well established that real estate offers at least a partial inflation hedge. The five-step procedure recommended in this article starts with that well-established foundation and translates it into a broader set of forecast distributions. At a time when the US economy may be in a state of Keynesian “true inflation,” investors need tools to address an uncertain future with few domestic historical parallels. The methods outlined here may represent one component of a more comprehensive approach to appreciating how inflation impacts real estate.

1. John Maynard Keynes, 1936, The General Theory of Employment, Interest and Money, reprint, Cambridge University Press, 2014. 2. Turner Building Cost Index. 3. Eugene F. Fama and G. William Schwert, “Asset Returns and Inflation,” Journal of Financial Economics, Vol. 5, No. 2, 1977, pp. 115–146. 4. NCREIF Property Index (NPI) data is used instead of the housing component of the CPI. A regression is also run with NPI returns as the dependent variable and four quarterly lags of NPI returns, the three-month Treasury bill yield, and the CPI minus the three-month Treasury bill yield as independent variables (instead of using lags of the CPI minus the Treasury bill yield, as Fama and Schwert proposed in the original article). 5. The housing component of the CPI should be highly correlated with the headline CPI. 6. The initial consensus forecast distributions along with adjustments may be thought of as the “prior” in Bayesian analysis. 7. Roger B. Myerson’s SIMTOOLS Excel Add-In is used for the calculations, but other simulation software can produce similar results. 8. The example in this section is not Heitman’s official house view; it is intended solely as an illustration. 9. The time period selected is based on data available from Real Capital Analytics; inflation (monthly log percentage changes; source: St. Louis Fed); unemployment rate (first difference of unemployment rate; winsorized at the 5th and 95th percentiles; source: St. Louis Fed); commercial property prices (Real Capital Analytics CPPI National All-Property Commercial Property Price Index). 10. Bloomberg is the source of the macroeconomic forecast distributions. 11. A slightly more complex version of this first approach could include combining a heavy-tailed variation of consensus forecast distributions with the shape of the distribution based on historical data. 12. For example, nonlinear relationships, non-normal distributions, and time-varying correlations can render static correlation matrices less meaningful. 13. An autoregressive model with two lags is used for simplicity—AR(2). 14. 10,000 simulations are created for each variable at the end of the horizon (4Q22).

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Featured in Institutional Real Estate Europe’s October 2019 issue: The European self-storage sector is at an earlier stage of development compared to its US counterpart Institutional investment in the sector has yet to take off in a meaningful way, despite …

Climate Risk and Real Estate Investment Decision-Making: New Report from ULI and Heitman

Heitman and the Urban Land Institute (ULI) developed a new research report focused on the risks that climate change poses to real estate and the measures that leading firms are taking today to mitigate those risks. Climate Risk and Real …

February 5, 2019

Packaged for delivery: e-commerce is fueling the asia-pacific region’s rapidly-developing logistics landscape.

Featured in Institutional Real Estate Asia-Pacific’s February 2019 issue: The logistics sector has become a global darling. Few real estate property types currently enjoy such rosy demand drivers. With e-commerce forecasts rising, big tech targeting a smartphone in every hand, …

February 1, 2019

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How to Create a Real Estate Investment Model in Excel

By Chris Cordone |

 Reviewed By Rebecca Baldridge |

March 28, 2023

What is a Real Estate Investment Model?

A real estate investment model is used to analyze the cash flows and returns to stakeholders from a real estate asset or collection of real estate assets. Unlike corporations, which use three statement modeling (income statement, balance sheet, and cash flow statement), a real estate model is primarily a cash flow statement with several ancillary pages on the revenues, costs, financing, metrics, and returns. Several assets can be grouped into one model; however, in most cases the analysis is done on a property-by-property basis.

Key Learning Points

• Real estate models have several purposes, but are principally intended to bring efficiency to the buying, managing, and selling of real estate assets.
• Models are mainly cash flow-based with very little, if any, accrual-based accounting
• The key drivers of a real estate model are rental revenues, operating expenses, and capital expenditures. Net operating income, as well as levered and unlevered cash flows, are the key measurement figures.
• Leases analysis is the most detailed component of real estate modeling.
• Like most transactions in finance, models can be built quickly for back of the envelope analysis or in great detail for transaction support.

Importance of Real Estate Modeling

In real estate, a successful deal is defined by paying the right price, choosing the correct capital structure, managing the asset at the highest level of efficiency, and selling at the right time and price. In order to compile and present that data for analysis, a model is needed.

The primary stakeholders in a real estate investment are the debt and equity holders. The analysis is geared toward satisfying their risk/return profiles. Let’s look at the debt investors first. They are making a loan based on the value of property. This is called loan sizing. However, once the loan is made, there must be cash flows to service the loan — the interest and principal payments. A cash flow analysis of the property is necessary to determine if the rent receipts, net of any building expenses, are sufficient to service the debt. The cash flow that debt holders look at to determine debt service coverage is called the unlevered cash flow, as it has not yet been affected by the financial structure.

The example below shows a model from NOI to levered and unlevered cash flow.

From the equity perspective, the model needs to do more than that since equity investors have a greater cost of capital, or required return, than do debt holders. The real estate model will look at historical cash flows to assess the asset is in terms of generating current cash flow.  Assumptions must then be made about the future cash flows. It’s necessary to forecast rental revenues and building operating expenses, as well as capital, or longer term, expenditures.  Since the equity holders’ returns come after the debt holders, we need to look at the cash flows after debt service. This is called the levered cash flow. It is the residual cash flow that goes to equity investors. Equity holders measure their returns using metrics such as IRR (Internal rate of return), MoIC (multiple of invested capital), and cash on cash yield (which measures the regular dividend payments)

The following is an example of the kind of analysis performed for equity investors. Access the free download to see

Step-by-Step Process to Real Estate Financial Modeling

1) Net Rental Revenues

Rental revenues are the key drivers and they are heavily dependent on the leases that are in place. A very detailed real estate model will have lease assumptions for each tenant.  Assumptions are also made for rent increases, as well as potential vacancies, and for credit losses (tenants not paying their rent). This analysis can be extremely complicated from a modeling perspective so it is typically only done once a buyer is serious about investing or by the asset manager who is responsible for keeping the property’s returns high. A modeler will also have to consider any ancillary revenues that are part of the property such as parking, concessions, vending, storage, etc.

Below is an example of detailed lease analysis modeling

2) Operating expenses

The cost of running the building day to day are the operating expenses. The main building expenses include insurance, property taxes, and common area maintenance fees (CAM). In addition, there are the costs of utilities for the rented spaces. Depending on the type of leases, these expenses are either fully paid by the lessee (renter), shared by the lessor and lessee, or are paid entirely by the lessor. This will have an impact on the model. In many buildings, the leases are consistent, so if one tenant is responsible for their share of the expenses, the other leases most likely work the same way. Expenses are shared by the pro rata share of the property’s rentable space.

The net of the rental revenues and the operating expenses is called the Net Operating Income, or NOI, and it is a very basic measure of the asset’s profitability.

3) Capital expenditures

There are expenses that are not considered part of the daily upkeep but instead, benefit the owners for the longer term. These are the significant upgrades to the property such as landscaping, new elevators, painting/refinishing, and structural elements such as roofs, parking decks, etc. These are slightly more discretionary than operating expenses so they are not included in the NOI.

In addition, there are a few other expenses that fall into the capital expenditure category that are related to how the owner manages the property and they are not considered day-to-day. When vacancies are rented or leases are renewed, a broker is paid a commission on the contract. That is considered long term since it benefits the life of the contract.  Furthermore, to attract new tenants there are several concessions that are made that involve improving the space for the new tenant. These can be upgrades to the infrastructure or funds to be applied to the buildout of the space.

The net of the NOI and the capex will deliver the unlevered cash flow, which we can use to begin our analysis to the stakeholders. Due to the unique tax structure of real estate assets, corporate tax is not paid but rather personal taxes are applied to the pass- through earnings.

4) Debt Schedule

The terms of the loan or loans must now be modeled so that we can determine if there is enough cash flow or cushion to support the loan. We would also need to determine the principal and interest payments to determine the levered cash flows, or cash flows post-debt service. An example of a debt schedule is shown in the graphic below.

5) Equity returns

The levered cash flows will enable us to look at what cash is left over for quarterly or annual distribution to investors. With the levered cash flows modeled out over the life of the investment, we can also determine what the overall return is to the equity investor using an IRR analysis. An example of an equity returns analysis is shown in the graphic that appears earlier in the blog post.

Types of Real Estate Models

Depending on the timing and the information available, a real estate model can be very basic or extremely detailed. With all forecasting, there is an element of guesswork and assumption.  With real estate, however, we have the ability to forecast according to the leases in the building. That means a very detailed model will often show monthly cash flows since that is how rent and operating expenses are handled. Debt holders will often require monthly service as well. Investors might not need to see monthly returns as their distributions are either quarterly or annually. But once we have a monthly model, it is easy to add quarterly or annual tallies.

For a transaction in the due diligence stage or an asset manager overseeing the efficiency of the property, a detailed lease-by-lease or tenant-by-tenant model is necessary. For any other purpose, a model can be built making broad assumptions about the revenues and costs according to both historical figures and market projections.

Real estate models are critical to the analysis necessary to buy, manage, and sell a property. These models are typically done on a monthly basis but can be quarterly or yearly. They can be extremely complex and model cash flows on a per-tenant or per-lease basis, or they can be higher level and forecast according to historical and general market trends. Either way, stakeholders in the deal will be able to see their potential returns and the risks that are inherent in the investment to determine if it is a good fit or if improvements can be made to enhance the returns. The Real Estate Analyst Course will teach you how to build a real estate investment model.

Additional Resources

Cap Rates in Real Estate

Investing in Real Estate

Real Estate Valuation 

Real Estate Financial Modeling

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The Impact of Inflation on The Real Estate Industry

Inflation is a critical economic factor that has far-reaching effects on various sectors. The real estate, title, and financial services industries are three of the most impacted. Understanding the correlation between inflation rates, the economy, and real estate values is essential for investors, homeowners, and policymakers. It is also critical knowledge for professionals and business owners in the real estate and title industries. This article provides a comprehensive overview of the impact of inflation on real estate prices today and future implications.

READ THIS EBOOK:  RIDING THE WAVES: How Interest Rates and Inflation Impact Real Estate

The Correlation Between Inflation Rates and Real Estate Values

Historically, there has been a strong link between the impact of inflation rates and real estate values. The most important factor is buyer or market expectations. In markets where future price expectations are higher, the market incorporates this expectation into today’s prices. This makes intuitive sense and drives interest rate expectations of investments. If buyers expect inflation rates to stay high, then future real estate values will be higher too. Investors seek to earn a “real” rate of return – over and above the rate of inflation.

This correlation can be further explained through several mechanisms:

1. Asset Appreciation – Inflation often leads to the devaluation of currency over time. Real estate, as a tangible asset, tends to appreciate in value during inflationary periods. This is because the cost of construction materials, labor, and land typically rises with inflation, leading to higher property prices.

2. Interest Rates – Central banks often respond to inflation by increasing interest rates. Higher interest rates can deter potential homebuyers from entering the market due to the increased cost of borrowing. However, this can lead to a decrease in demand, which may eventually put downward pressure on real estate prices.

3. Hedging Against Inflation – Real estate is often considered a hedge against inflation. Investors often turn to real estate to preserve their wealth during times of rising prices, as it offers a relatively stable and tangible asset that can retain its value.

4. Rental Income – Rental properties can provide a steady stream of income for investors. During inflationary periods, the impact of inflation can cause rental income to increase to keep pace with rising living costs.

5. Reduced Construction Activity – in higher inflationary environments, the cost of capital is higher too. This means it will cost more to build housing, which will (eventually) reduce the amount of new inventory coming online. This shortage will result in higher future real estate prices.

What to Expect in the Future?

Forecasting future interest rates is crucial for predicting the direction of real estate prices. The challenge, however, is that predicting interest rates is very difficult. There are many different variables to consider. World events can also have an impact at any time, adding further complexity to any interest rate forecast. Regardless, it is wise to stay current with various influential industry leader’s forecasts. Much can be learned by reading the reports. More importantly, what is critical to understand are market expectations. In the end, these have just as much of an impact on real estate market values and industry volume.

Here are three different reliable sources worthy of tracking to stay informed on the current impact of inflation. Each of these institutions publishes interest rate and inflation forecasts. These can then be used to have a better idea of what future real estate transactions will occur. Expectations then can directly impact the volume of real estate and title industry volume.

1. Federal Reserve Economic Projections – The Federal Reserve regularly publishes its economic projections, including the expected path of the federal funds rate. These projections provide insights into the central bank’s monetary policy decisions and can be accessed on the Federal Reserve’s official website .

2. Financial Institutions – Major financial institutions, such as JPMorgan Chase and Goldman Sachs, often release interest rate forecasts. These forecasts are based on in-depth economic analysis and can be found on the respective institutions’ websites.

3. Economic Research Firms – Firms like Moody’s Analytics and Oxford Economics specialize in economic forecasting. They offer detailed reports on various economic indicators, including interest rates, which can be valuable for predicting real estate market trends.

The Relationship Between Real Estate Values and Title Policy Issuance

The volume of title policy issuance is closely tied to the health of the real estate market. Title insurance is a crucial component of property transactions, as it provides protection against any unforeseen legal issues related to the property’s title. Therefore, the volume of title policy issuance can serve as a reliable indicator of business volumes in the real estate and title insurance sectors.

During periods of robust real estate activity, such as a seller’s market with high demand, the volume of title policy issuance tends to increase. Conversely, during economic downturns or periods of reduced real estate activity, the volume of title policies issued may decline.

To illustrate this relationship, you can refer to data from the American Land Title Association (ALTA). ALTA regularly publishes reports and statistics related to the title insurance industry, including data on policy issuance volumes.

Anticipating Future Business Volumes for Title Companies

The correlation between changing real estate values and the volume of title policy issuance can be utilized as a forecast tool for title companies. By monitoring trends in real estate prices and market activity, title companies can anticipate their business volumes in the coming years.

For example, if real estate prices are expected to rise due to inflation and there is a surge in homebuyer demand, title companies can prepare for increased policy issuance. Conversely, in a scenario where economic uncertainties lead to a slowdown in the real estate market, title companies may need to adjust their operations accordingly.

Furthermore, title companies can also adapt their marketing and pricing strategies based on the anticipated demand for title insurance policies. This proactive approach can help title companies remain competitive and financially resilient in changing market conditions.

Forecasting in Today’s Economic Environment

Today, there is a big problem with everything we shared above under a “business as usual” economy. Change used to be more predictable, and markets had more of an expected set of operating conditions. Then, along came COVID, and everything changed. Markets were significantly disrupted and are expected to stay in a status of recovery for many years to come. Significant structural changes occurred that have permanently shifted how goods and services are purchased, where people live, and how they work. Together, these changes have made it nearly impossible to predict interest rates or inflation with any great accuracy.

There is a term that summarizes today’s challenging market conditions quite well – VUCA. This acronym stands for Volatility, Uncertainty, Complexity, and Ambiguity. VUCA plays havoc on interest and inflation rate forecasting. Learn more about this market condition, Overcome VUCA With an IT Systems and Automation Strategy . Despite these challenges, companies can overcome VUCA by adopting modern systems and automation strategies. Organizations need to invest in new systems, processes, and automation strategies such that change can be executed quickly and accurately.

Artificial Intelligence will also play a greater role in helping with how to incorporate the myriad of factors potentially impacting future interest and inflation rates. This information, combined with an agile system and workflow strategy, offers the potential for real estate and title companies to rise to the challenge and deliver superior financial performance.

Inflation has a profound impact on real estate prices. Understanding this relationship is essential for making informed investment decisions and predicting future market trends. By examining historical data, monitoring forecasts of interest rates, and recognizing the connection between real estate values and title policy issuance, stakeholders in the real estate and title insurance industries can better prepare for the challenges and opportunities that lie ahead.

As with any economic analysis, it’s important to acknowledge that future outcomes are subject to various factors and uncertainties. Therefore, it’s advisable to stay informed, continuously monitor market conditions, and adapt strategies accordingly.

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A Dollar's Worth: Inflation Is Real

Understanding the reality of inflation can help consumers make decisions in personal finance. Learn more about inflation, how it’s measured, and how the inflation rate is calculated in the December 2021 issue of Page One Economics: Focus on Finance.

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Is Real Estate Real?

By: Lynne B. Sagalyn, Andrew Ang, Rona Smith

In this case a consultant to pension funds ponders the unique opportunities and risks she faces as she considers adding real estate to the fund's portfolio for the first time. While the fund already…

• Length: 17 page(s)
• Publication Date: Jul 8, 2011
• Discipline: Finance
• Product #: CU156-PDF-ENG

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In this case a consultant to pension funds ponders the unique opportunities and risks she faces as she considers adding real estate to the fund's portfolio for the first time. While the fund already owns REITs, its trustees believe that direct real estate investment could offer protection against the threat of inflation. But the category of real estate includes a wide range of distinct subclasses, including office, retail, industrial, hotel, and rental apartments. In analyzing the situation, students must determine which of these sectors provides the best hedge against inflation, and the risks and returns of the asset class as a whole.

Learning Objectives

To illustrate the value and risks for pension funds of direct investment in real estate versus investing in REITs. Considers various sectors of real estate market.

Jul 8, 2011

Discipline:

Geographies:

United States

Industries:

Financial service sector, Investment management services

Columbia Business School

CU156-PDF-ENG

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The World’s Greatest Real Estate Deal Analysis Spreadsheet™ Version 2024.02

Watch the Video Overview…

All Other Deal Analysis Spreadsheets Are Wrong…

If you’re not accounting for:

• Reserves (including the return you earn on your reserves)
• All 4 areas of returns (appreciation, debt paydown, depreciation and cash flow)
• Return on equity (actually your True Net Equity™ after expenses)
• The cost of accessing equity, and
• Negative cash flow (if applicable)…

…when analyzing deals, then you’re analyzing deals all wrong .

The World’s Greatest Real Estate Deal Analysis Spreadsheet™ is the only spreadsheet that analyzes deals correctly… all other spreadsheets come up short and do not correctly account for everything above.

Track Your Investment Property Return Over Time

Plus, The World’s Greatest Real Estate Deal Analysis Spreadsheet™ allows you to track your investment and returns over time.

Enter in how much your property appreciated each year that you own it and see the spreadsheet update all your returns.

Do the same with debt paydown, cash flow and depreciation. Easily track how your rental property is performing by updating the spreadsheet with numbers for each year in the Overrides tab.

Simpler to Use

The new rental property analysis spreadsheet is simpler to use.

We’ve moved all the information we rarely look at when analyzing a deal from the main Inputs & Dashboard tab to the Overrides tab.

We took the 9 most important decision-making metrics, made them easy-to-read charts that take up 80% of the main tab.

More Powerful Than Ever

First, we simplified the input for making a decision if the property is a deal or not.

But, we also empowered you to do what you’ve never been able to do before in a deal analysis spreadsheet… override EVERYTHING .

The new rental property analysis spreadsheet is more powerful than ever because you can edit just about any input, for any year. This allows you to model incredibly complexed and nuanced situations like changing tax situations, changing costs to sell or refinance depending on the year, plus much, much more.

Improve Cash Flow

Use the spreadsheet and our comprehensive list of 88 strategies to improve cash flow on your rental properties to ensure that you’re only doing the best, most profitable deals.

Everything on One Screen

Use “Full Screen Mode” in Excel with 100% zoom to see the entire Inputs & Dashboard on one, reasonably-sized screen (1920×1080).

Easily Print to One “Landscape” Page

Want to print your deal analysis to share it with others? Easily print the entire Inputs & Dashboard on one “landscape” page.

Legal-sized paper is better, but letter-sized paper works.

Simplified Inputs

Everything you need, but nothing you don’t.

Color coded:

• Manilla Boxes: Inputs
• Gray Background with Blue Text: Calculated

Advanced Input Overrides

Feel empowered: override just about ANY variable using the Overrides tab.

Sparklines help understand what is happening at a glance.

Drill down more if needed and see Return Quadrants™ and more calculated.

Different Tools

• The World’s Greatest Real Estate Deal Analysis Spreadsheet™ = analyzing a deal/property.
• Real Estate Financial Planner™ = analyze/compare entire investment strategy/strategies, goals and risks.

There are plenty of different real estate investing strategies … some better than others depending on your specific goals. Find out which works for you with the Real Estate Financial Planner™ software.

The Real Estate Financial Planner™ software gives you unprecedented insight into how your entire strategy of stocks, bonds, commodities and, of course, rental properties performs in a variety of market conditions.

It answers questions about how quickly you can achieve financial independence and how much risk you have with each strategy you’re considering with questions like:

• Should I pay off my rentals? Sell some to pay off others?
• Should I refinance? Cash out refi? Buy more properties when I refi? Invest in something else?
• Should sell properties? Buy more properties when I sell? Invest in something else?
• What if I stop working on this date?
• What if I take social security on a specific date and/or buy an annuity?
• Plus, much much more…

Export to Real Estate Financial Planner™

One-click export will allow you to go straight from deal analysis to full portfolio and strategy analysis.

Easily Export Charts for Presentations

Raising money?

Sharing your deal with a partner, spouse, lender, or banker?

Teaching classes?

Easily export charts from The World’s Greatest Real Estate Deal Analysis Spreadsheet™ to Microsoft Power Point or Microsoft Word.

Export as an image like this:

Or, export as a dynamic chart that updates when you make changes to the original spreadsheet like this:

9 Primary Charts

Monthly Cash Flow – Year 1

Return in dollars – year 1, return on investment – year 1, returns if sold (roi and irr) – years 1-20, cash on cash roi & cap rate – years 1-5, return on true net equity™ – years 1-20, key metrics – annual – year 1, non-loan expenses – year 1, equities and cost-to-access – years 1-20.

Shows the monthly value of:

• Cash Flow (after all expenses)
• Cash Flow from Depreciation™ – Just the cash flow from your depreciation benefit (after taxes).
• True Cash Flow™ – Which is the sum of cash flow and Cash Flow from Depreciation™ .

Cash Flow tends to be the primary metric folks look at when analyzing a rental property.

This shows you the Return in Dollars Quadrant™ values (in chart form) for year 1.

That’s the dollar amount of each of the following in the first year:

• Appreciation
• Debt Paydown , and
• Cash Flow from Depreciation™

There is more to buying rental properties than cash flow. This chart shows you this succintly.

It answers the questions of:

• How much did the property appreciate in year 1?
• How much cash flow did you generate in year 1?
• How much did you pay down in debt in year 1?
• How much cash flow did your tax benefits of depreciation generate?
• What was the TOTAL benefit (in dollars) you gained by owning this property in year 1? The number at the top in bold.

Then, it does the same numbers but also includes the amount you earned from reserves.

We have 2 “flavors” of reserves depending on how people choose to handle their reserves: 6 months of reserves in an account that earns 1% per year (RIDQ+R6™) or 12 months of reserves in an account that earns 8% per year (RIDQ+R12™). For more information on this concept, check out Everything You Learned About Deal Analysis is Wrong – ROIQ+R™ .

In the last chart, we showed the DOLLAR AMOUNT from appreciation, cash flow, debt paydown and Cash Flow from Depreciation™ … but this chart we show you that dollar amount divided by the Total Invested .

In the case of Return on Investment Quadrant + 6 Months Reserves ( ROIQ+R6™ ) and Return on Investment Quadrant + 12 Months Reserves ( ROIQ+R12™ ) it is the dollar amount divided by the Total Invested PLUS… either 6 months of reserves or 12 months reserves.

Astute investors will recognize that the “cash flow” ROI here is the same calculation as Cash on Cash Return on Investment .

This chart shows you the returns you’d earn if you sold the property calculated 3 different ways.

First, the total returns you earned for the entire holding period (after all sales costs) divided by the Total Invested and the number of years you held the property. This is the Annualized Return on Investment (aka Annualized ROI ).

Second, the same calculation except the compounding rate of return version. This is the Compound Return on Investment (aka Compound ROI ).

And finally, the Internal Rate of Return ( IRR ) calculation that better accounts for WHEN you put money in and take money out of the property to calculate the return you’re earning on the investment. One could argue that this is the best return to use.

Many investors who plan on selling their property after holding it for a period of time will find this chart invaluable.

In the Return on Investment chart above, we showed Cash on Cash Return on Investment as one of the returns.

It is so important to some investors that we list it again here and compare it to Capitalization Rate (aka Cap Rate ).

Most investors prefer to use Cash on Cash Return on Investment , but a good number of investors prefer to use Cap Rate in their decision-making process.

So, here’s both.

After year 1, taking the dollar amount of return earned in that year and dividing it by the initial Total Invested when you bought the property becomes a less important measure.

One could easily argue that taking the dollar amount that you earn in that year and dividing by the equity you have in the property that year is a better measure of the return you’re earning on the amount you have in the deal.

Further, one could argue even better that it is not the return you’re earning on gross equity, but rather, the return you’re earning on the money you’d walk away with after sales costs (real estate commissions, closing costs, depreciation recapture tax and capital gains tax).

Really, what you want to know is the return you’re earning on the money you’d get if you exited the deal. If you sold the property, this is the return you’d need to earn on another, different investment to do better than this property.

This chart shows you the return you’re earning from the 4 component returns (appreciation, cash flow, debt paydown and Cash Flow from Depreciation™ ) divided by True Net Equity™ (equity after all those sales costs) for the first 20 years.

It also shows the total return for all 4 component returns divided by True Net Equity™ .

Some real estate investors would like to know Gross Potential Income ( GPI ), Gross Operating Income ( GOI ), Operating Expenses ( OpEx ), and Net Operating Income ( NOI ) for year 1.

So, that’s what this chart shows.

Often investors will want to get a feeling for the expenses on the property. This chart shows the non-loan expenses for the property in the first year.

It also includes the dollar amount for the vacancy allowance as well.

Some real estate investors will want to either sell the property or refinance the property to access the equity in the property at some point in the future.

This chart shows the amount of equity (after all sales costs… which we define as True Net Equity™ ) that they’d walk away with if they sold the property.

It also shows the amount of equity they could walk away with if they did a cash out refinance of the property.

Additionally, it shows the cost-to-access the equity in both cases as a percent of the money they’d walk away with.

Did we miss a key metric you use to make a buying decision when analyzing deals? Have suggestions? Found an error? Email [email protected]

You can still drill down into the Overrides tab.

Modeling Variable Returns

Appreciation isn’t always 3% per year (or whatever number you’re using). One year it may be 3%, then next 0%, the next 6.13% and so on.

With The World’s Greatest Real Estate Deal Analysis Spreadsheet™ you can use formulas for OVERRIDES to model variable returns for things like:

• Appreciation Rate
• Rent Appreciation Rate
• Maintenance Rate

And, you can see how your investment might perform with variable returns.

For example, you could use =NORMINV(RAND(),0.03,0.01) to override Appreciation Rate and Rent Appreciation Rate .

The overrides might look like this:

And, the new charts on the Inputs & Dashboard might look like this:

Professional Sponsorship Licensing

If you’re a professional that works with real estate investors (real estate agents/brokers, lenders, Accountants/CPAs, and financial planners) and want to license The World’s Greatest Real Estate Deal Analysis Spreadsheet™ to have your contact information on it so you can give it to your clients, we offer the ability to license it and have your contact info on it.

Available in Two Editions: Silver and Gold

The Silver Edition includes everything above with the two tabs: Inputs & Dashboard and Overrides .

The Gold Edition has yet to be released but will have extra tabs and have additional dashboards/insights specifically for:

• Partnerships – Extra dashboard and insight for modeling buying your property with more than one partner with, possibly, unequal investments, cash flow and equity allocations between the partners.
• Return Quadrants™ – A summary tab showing a variety of Return Quadrants™ for your property.
• Lease-Options – Extra dashboard and insight if you’re considering selling the property to a tenant-buyer using a lease-option.
• Should I Sell My Property – Extra dashboard and insight if you’re considering selling your property.
• Should I Refi My Property – Extra dashboard and insight if you’re considering refinancing your property.
• Track Your Property’s Performance – Because we now allow you to OVERRIDE just about any variable for any year, you can use TWGREDAS™ to keep track of how your property has performed over time and calculate returns based on actual performance (not just pro-forma).

James plans to work on the Gold Edition sporadically, irregularly over the next year or two. He accepts bribes (of significant size) to move the thing you need toward the top of the list. Pay for and schedule a “support” call with James to discuss what you want moved toward the top of the list.

Or, if you need support with your specific situation or property, book a paid support call and we can go over the spreadsheet together.

Other Spreadsheets

The spreadsheet on this page is obviously for deal analysis, but we also offer other spreasheets.

Plus many more…

Real Estate Investing Classes and Videos

Check out our free real estate investing classes as well to learn more.

4 thoughts on “Real Estate Deal Analysis Spreadsheet”

Hey James, we’re both ostensibly pretty transactional people but you’ve got so many moving parts going in so many directions, I can’t keep up. I’d love to just buy some of your expertise if we could figure out how. First off, I’m not your targeted demographic. I’m not an RE investor, but an overly analytical home buyer (Nomad?) buying my own (non-conventional) residence — using investor methods. I have a bunch of spreadsheets — but would love to have yours if I can edit it — especially the macros — for my residential purposes. Would also love to buy downloadable files or documents on “creative deals” owner carry, wraps, subject to, etc. that I can share with a researcher. Thanks, Dru

Thanks for the comment Dru. Sorry you’re not able to keep up.

Yes, we definitely focus on providing resources for buy-and-hold real estate investors, Nomads™ and house hackers. That is our target audience.

You’re welcome to download Brian’s spreadsheet (aka The World’s Greatest Real Estate Deal Analysis Spreadsheet). He has it locked down so that anyone who is not an Excel expert can’t mess up the formulas and macros. If you’re an Excel expert you, of course, know how to unlock it so it is fully editable. If you need to ask Brian how to unlock it, you probably shouldn’t be messing with the formulas.

I don’t think I offer downloadable files or documents for “creative deals” like owner carry, wraps, subject to for you to share with your researcher that I can think of except maybe the spreadsheet I created for modeling buying creative properties using marketing (linked below).

Really great resources and lessons. Even though I am from Europe and focus on flats (its europe …), most of the things are applicable.

Though I really had to dig and change the excel as Depreciation does not exist in Europe. We have here buildings old few centuries and people are still living in them. Also tax on RE is like nonexistent here 🙂

If you have more ppl watching from EU, maybe add a feature to disable the depreciation part of the excel.

Kind Regards, Kamil.

Thanks Kamil. Glad you enjoyed it. We have VERY, VERY few people from EU visiting this website but glad you were able to make it work. Enjoy!

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Find what you need to study

2.4 Price Indices and Inflation

6 min read • december 18, 2022

Jeanne Stansak

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Consumer Price Index —a measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care. (Source: investopedia.com ). The basket of consumer goods that we are talking about is a sample of goods and services that are typically bought by your average household.

Inflation —the general rising of prices in a market or aggregate economy over time.

Deflation —the general decrease of prices in a market or aggregate economy over time.

Disinflation —a decrease in the rate of inflation

Inflation Rate —the percent change in aggregate price level across an entire economy in one year.

Real variables —variables where the effects of inflation have been factored out.

Defining Inflation

So far, we've looked at GDP, which shows production, and unemployment, which describes the labor force. Inflation is another important economic concept that measures the general level of prices in an economy over time. Inflation is usually measured by the percentage change in a price index , such as the Consumer Price Index (CPI) or the GDP deflator .

Inflation occurs when the general level of prices in an economy is rising. It is expressed as a percentage and represents the rate at which the general price level is increasing over time. For example, if the inflation rate is 3%, it means that the general level of prices in the economy is increasing at a rate of 3% per year.

Deflation is the opposite of inflation and occurs when the general level of prices in an economy is falling. Deflation is expressed as a negative percentage and represents the rate at which the general price level is decreasing over time.

Disinflation is a slower rate of inflation, or a slowing down of the rate at which the general price level is increasing. For example, if the inflation rate was previously 5% and it slows down to 3%, it would be considered disinflation .

Inflation, deflation, and disinflation are all important economic concepts that can have significant impacts on an economy. Inflation can affect the purchasing power of money, as prices for goods and services rise faster than wages or incomes. Deflation can also have negative effects, as it can lead to a decline in demand and a decrease in economic activity. Central banks and governments typically aim to keep inflation within a certain range, as too high or too low of an inflation rate can have negative impacts on the economy. For the United States, this is roughly 2%. We'll talk about how the government and economic policy-makers target these variables later in the course - get excited!

Here's a graph of what the inflation rate has been throughout the latter half of the 20th century. Note that in general, recessionary periods bring with it lower inflation. The exception to this is the recession in the 1970s. This is because the economy experienced stagflation , in which prices rose and the economy contracted. This was because of oil crises.

The Inflation Rate in the United States over time. Source: FRED (Federal Reserve Economic Data)

Price Indices and the CPI

A price index is a statistical measure that reflects the changes in the general level of prices for a basket of goods and services over time. Price indices are commonly used to measure inflation and deflation, as they provide a way to track changes in the general price level of an economy.

One common price index is the Consumer Price Index (CPI), which measures the changes in the prices of a basket of goods and services consumed by households. The CPI is calculated by the Bureau of Labor Statistics in the United States and is used to measure the changes in the cost of living over time.

The CPI is based on a market basket of goods and services that is representative of the purchases made by households. The market basket includes a wide range of goods and services, including food, housing, clothing, transportation, and medical care. The prices of these goods and services are collected and used to calculate the CPI.

To calculate the CPI, the Bureau of Labor Statistics collects price data for the goods and services in the market basket from a sample of retailers, service providers, and other sources. These prices are then used to calculate the cost of the market basket at a specific point in time, known as the base period . The CPI is then calculated by comparing the cost of the market basket in the current period to the cost of the market basket in the base period , and expressing the change as a percentage.

The CPI is used to measure the changes in the cost of living over time and is often used to adjust wages, Social Security payments, and other income for inflation. It is also used to make international comparisons of inflation rates and to inform policy decisions related to monetary policy .

One source of bias in the CPI is the substitution bias , which occurs when consumers switch to cheaper alternatives as the prices of certain goods and services rise. If the CPI does not take into account these substitutions, it may overstate the actual increase in the cost of living .

Practice Problems

How to calculate the cpi.

The CPI defines a base year in which all other index values are based on. At the base year, the value is 100, and all other numbers represent a percent increase or decrease from the base year. For example, a CPI of 110 implies that prices have risen 10% since the base year. To find the CPI for a given year, divide that market basket by the base year and multiply by 100 convert to a percent.

To calculate the inflation rate , just calculate the percent change in prices or in CPIs. Percent change is defined as (final - initial) / initial * 100.

Sample Problem

United States Market Basket - 2016

United States Market Basket - 2017

United States Market Basket - 2018

The first step in calculating either the CPI or the inflation rate is to figure out the value of each market basket. Once you have calculated the market basket, then you can figure out both the CPI for each year and the inflation rate from year to year.

To find the value of the market basket for each year, you simply multiply Price x Quantity for each good and then add all those amounts together. Let's use 2016 as the base year in this example.

Market Basket Value for 2016 = ($3 x 10) + ($2 x 10) + ($5 x 10) = $100

Market Basket Value for 2017 = ($3.25 x 10) + ($3.50 x 10) + ($5.25 x 10) = $120

Market Basket Value for 2018 = ($3.50 x 10) + (3.50 x 10) + (5.50 x 10) = $125

💡When calculating the CPI for the base year, you are always going to get a 100 as the answer. The reason for this is because you divide the value of the market basket for the base year by itself.

2016 CPI = ($100/$100) x 100 = 100

2017 CPI = ($120/$100) x 100 = 120

2018 CPI = ($125/$100) x 100 = 125

2016 to 2017 Inflation Rate = ((120-100)/100) x 100 = 20%

2016 to 2018 Inflation Rate = ((125-100)/100) x 100 = 25%

Key Terms to Review ( 15 )

Base period

Bureau of Labor Statistics

Central Bank

Consumer Price Index (CPI)

Cost of living

Disinflation

GDP deflator

Inflation Rate

Monetary Policy

Price index

Purchasing Power

Real variables

Stagflation

Substitution bias

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A Case Study On: The Return of Inflation

• January 14, 2021

After a forty-year bond market rally from a high of 18% in 1982 to the current 0.70% low today, we struggle to get excited about bonds in the short or medium term.

Now What to Do?

Bonds have always been there to diversify from the volatility of the stock market.  The idea was, and is, when stocks go down, bonds go up, and vice versa.  In 2020, we saw that historical relationship break.  When stock markets crashed in March, the Bond Market went down as well until the Federal Reserve and Bank of Canada “saved” the Bond Market.

Another attribute is that bonds are there for capital preservation despite having little yield, which is a very important attribute to have when dealing with risk or asset allocation of clients’ wealth.

When we look beyond those two benefits, we struggle to find many opportunities in the credit or interest rate market due to historically low interest rates. As well, we have seen the central banks’ interventions into the credit markets suppress credit spreads.

Where Do the $300 Trillion in Global Debt Markets Go From Here?

For the past two decades, analysts, economists and central banks have forecasted incorrectly that global bond yields should yield at higher levels. The ongoing combination of technological disruption, global free trade expansion and our aging population has caused deflationary pressure, therefore causing yields to be suppressed.

Why It’s Different This Time

For the past decade, central banks have “pumped” the system with a massive injection of money for two reasons.  Firstly, for the public markets to function properly.  Secondly, they are trying to keep businesses afloat (large and small).  The former worked wonderfully, the latter not so much.

When central banks increase liquidity, they release funds to the banking sector. The problem has been that this money doesn’t leave the bank and get lent out into the economy. The banks are too scared to lend or it is too expensive (from a capital perspective) for them to lend. So, the money never gets into the hands of main street and the commercial banks just buy government bonds to keep on their balance sheets.

For the economy to recover, the commercial banks need to get money into people’s hands. This could happen.   More on that later .

The Central Banks are also trying to reflate the economy. To get inflation, you need several conditions in place.  Or to say it differently, you need specific ingredients to bake an inflation cake:

1. Central Banks Are Slowly Losing Their Independence

With record amounts of sovereign debt, there is very little chance that central banks will not be influenced politically. There is a view that the only way out is to inflate the debt away.  This occurred in 1948.  After WWII, the Allied countries were left with a massive amount of debt.  They had little chance of paying it back.  So, they used inflation as a monetary trick.  After the war, there was tremendous pent-up demand resulting from rationing, consumer high savings rates and European rebuilding (Marshall Plan). All this caused inflation over several years. They used higher inflation to lower the outstanding debt since future dollars are worth less than present dollars, thereby dropping the real debt ratios.

2. Labour Market and Class Division

There will be a new agenda focusing on closing the divide between the upper class and the middle class.  We have started to see wage increases. As an example, minimum wage workers have seen their wages double. We believe we are going to hear more of new labour union creation for the working class to extract more profits from their companies. An example is the organizing of Amazon workers.

3. Global Trade

A) modern trade agreements and reshoring.

We have seen so much deflation coming from global trade agreements set in the 1990s and 2000s. The Agreements in place are starting to unwind.  We are seeing western countries renegotiate trade deals that encompass not only economic benefits but must have social benefits as well, which will lead to higher prices for goods.  Manufacturing is also looking to bring back their production to their own country, which is called reshoring. Goods will be more expensive.

b) Cold Trade War with China

Western economies are starting to re-evaluate their relationship with China due to trust issues. Cybersecurity, political intervention by the Chinese state and uneven access to their markets is causing the West to re-evaluate our relationship.  This will mean that companies will try to find other options such as reshoring manufacturing.  This will lead to higher prices.

4. ESG (Environmental Social Governance), Social Lending, Justice and Green Programs

This type of policy is now in vogue. Even though it is just and honourable to consider these social lending polices, from an economic perspective the policies will force higher prices since these policies have a cost which must be shared throughout society.

5. Central Banks’ Policies Mandate Inflation

Most central banks have abandoned low inflation policy.  The US Federal Reserve has now installed a policy where inflation targets should be at minimum 2%.  Central banks have said they will not tighten monetary policy until they see a complete recovery.

6. Asset Prices Are Now Inflationary

Whether it is housing, the stock market or bitcoin, all asset classes are now reaching bubbling levels that will trickle down into the Consumer Price Index (CPI) inflation calculation.

7. Interest Rate Curve Suppression ( Not Happened Yet )

There has been recent discussion on central banks trying to control and pin down long-term interest rates by purchasing unlimited amounts of bonds to keep the interest rate curve low. If this does happen, inflation will certainly rise.

8. Macro Prudential Regulation ( Not Happened Yet )

As previously mentioned, central banks will be pressured politically.  The main issue is that money expansion is not getting to the consumer or small business. There is early talk that governments could force banks to lend by guaranteeing all loans to their customers.  Or governments could force their central banks to buy outstanding federal debt, thereby creating money by fiat.  (Most modern currencies are fiat or paper currencies that are not based on a commodity such as gold.) Regardless, if this does happen, inflation becomes a real issue.

We do believe reflation is on the horizon.  We believe that inflation will easily move from 1.5% to potentially 3.0% in six months. Over time, we think that higher inflation is a bigger risk than lower inflation. If that occurs, it is a headwind for the bond portfolio.

With this in mind, we have been in a defensive mode since mid-2020 with our bond portfolio by doing the following:

• Lower term-to-maturity (shorter duration):  3.5 years versus 7 years for the index
• Credit exposure primarily in the 1 to 3 year area
• Larger weight towards REITs – sector trades at a discount to its Net Asset Value
• Lower weight to High Yield – sector shows little value versus risk.
• Possible purchase of inflation index-linked bonds in near future.

(As a reminder we can own 20% of the bond portfolio in higher risk assets such as convertibles, real estate investment trusts, preferred shares, and non-investment grade bonds.)

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Santa Clara County regional housing indicators

Posted by ft Editorial Staff | Aug 21, 2023 | Bay Area , Charts , Forecasts , Mortgages , Real Estate | 6

Santa Clara County was well on its way to a full housing recovery going into 2020, pre-pandemic. Residential construction was booming and buyer incomes were rising at a much quicker pace than the rest of the state. However, annual home sales volume had remained level-to-down since 2012, decreasing each year since 2018.

The past decade of Santa Clara’s successes were due to its strong  jobs market , particularly in the Silicon Valley area. However, the region’s high cost of living, reflected in steeply rising home prices, is a heavy drag on buyer demand, consistently holding back sales volume.

Then, in 2020, the economic response to the global pandemic alongside the underlying recession caused job losses to ripple across the state. To counter job losses, mortgage rates were dropped to historic lows which, along with homebuyer fear-of-missing-out (FOMO), reduced available sales inventory and pushed home prices into bubble territory. 

Then, in 2022, interest rates jumped, causing  buyer purchasing power  to plummet, and buyers and sellers to retreat. These rising interest rates heralded the  2023-2024 recession , now causing consternation for real estate agents as they face greatly reduced sales volume, home prices, leasing and mortgage originations.

While today’s high levels of home equity and existing FRMs will cushion the coming rise in foreclosures from being as severe as what followed the 2008 recession, expect an anemic housing market during the next two-to-three years.

Look for the recovery to consistently gain momentum here in Santa Clara around 2026. In the meantime,  recession-proofing your practice  will ensure the continuing success of your real estate career during the coming slump in fee transactions.

Updated August 21, 2023. Original copy posted August 2014.

Home sales volume heads down

Chart update 08/21/23

Constrained by tight inventory and high home prices, the path to transaction fees is narrow for agents practicing in the Santa Clara region. However, 22,900 home sales closed in Santa Clara County during 2021, a rapid 37% increase from 2020. This significant increase followed years of flat-to-down sales volume in the region.

Yet, the jump was temporary. Home sales volume declined 29% from the prior year in 2022, a return to “normal” pre-pandemic levels. Worse, as of May 2023, year-to-date (YTD) sales volume is 36% below 2022 and a whopping 42% below 2019 — the last normal year for sales volume.

The main reason for Santa Clara’s typically flat-to-down home sales volume? Home prices in the Santa Clara/San Jose area are inflated well beyond income raises, sending end users further afield where they are able to buy more home for the same amount of money. Held down by strict zoning regulations , inventory is unable to rise to meet homebuyer demand, meaning buyers end up paying more and more each year for the same home. These unhealthy, shuttering price gains are not what local real estate agents want.

Personal incomes have risen at a much slower pace than home prices. Personal incomes rose 7.9% in 2020 in the county. While this is a significant annual increase, it was well below the year-over-year increase in Santa Clara County home prices that year. After years of rising, home prices have hit a peak.

Expect sales volume and prices to bottom following today’s as-yet undeclared recession. Homebuyers will sit out the market until it’s clear prices have bottomed, likely to occur around 2024-2025.

Related article:

Agents and brokers: recession-proof your life

Inventory returns to historic lows

Multiple listing service (MLS) inventory had risen from the historic low reached at the end of 2021, but quickly reversed course in the second half of 2022. After two years of steep decline, for-sale inventory in Santa Clara averaged 25% below a year earlier as of May 2023. A symptom of buyer reluctance to list on top of the region’s already low inventory, today’s low inventory continues to keep price declines somewhat in check.

Looking forward, expect inventory to climb in 2023-2024. Recent interest rate increases have slashed buyer purchasing power , making it nigh on impossible for mortgaged homebuyers (and sellers seeking to purchase replacement property) to compete. Along with high inflation, the signs are pointing to a rapidly approaching downturn in the housing market — of which homebuyers and sellers are well aware. Today’s seller’s market has fully tipped, with prices to follow heading into 2023 as inventory grows and homebuyers choose to wait out the declining market.

Turnover rebounds following pandemic

Chart update 11/09/22

It’s no surprise Santa Clara County’s homeowner turnover rate is relatively stagnant, given the county’s resulting flat home sales volume. With home prices now in a free fall, it’s simply not the prudent time to buy in 2022-2023. However, the area’s solid jobs recovery has ensured new residents continue to pour in at a rapid rate, and this added stress is shown in the high renter turnover rate. In 2017, one-in-four renters moved in Santa Clara, rising slightly each year since 2019.

Santa Clara’s quick pace of population growth will help churn Santa Clara County’s turnover rate, first as increased renter turnover followed by a rise in homeowner turnover – once residential construction catches up to the demand. The caution in these forward observations is the tech and information bubble developing in the area and whether it will come to the point of bursting and putting end to further job growth in the 2023 recession.

Homeownership feels the pressure of high prices

While varying greatly from quarter to quarter, Santa Clara County’s long-term homeownership rate trended downward from 2005 through 2015, when homeownership bottomed at a very low 45%. Since then, the region’s rate of homeownership continues its volatile swings, at a high 53.3% in Q1 2023.

The rest of the state has experienced a swift decline in homeownership since the 2008 recession, peaking at over 60% in 2006, at 55.3% in Q1 2023. Santa Clara’s long-term rate of homeownership is mostly stable, due both to its successful jobs market  and  high home prices . Elevated prices keep homeowner turnover from rising to unsustainable levels (as occurred across the state during the Millennium Boom). Likewise, Santa Clara County’s strong jobs market bolsters its homeownership rate, which will be tested in the next couple of years as the state continues to claw its way out of the ongoing recession.

Regional construction boom defies expectations

Chart update 02/16/23

After a setback year in 2021 in Santa Clara County, residential construction starts of all types took off during 2022. Single family residential (SFR) starts jumped 52% from the prior year, while m ulti-family rising 33%.

Santa Clara County’s high  cost of living makes it more cost-effective to reside in a multi-family dwelling with communal amenities, as opposed to a large suburban SFR. And while multi-family construction has overall increased since the Millennium Boom years, it’s still insufficient to keep up with demand from the region’s ever-growing population.

SFR and multi-family starts will likely hit their peak following the recovery from the 2023 recession. This will be helped along by new legislation intended to combat the growing housing shortage by fostering more construction.

Santa Clara jobs nearly recovered

Without access to quality jobs, residents cannot qualify to buy or rent. Thus, employment is a leading indicator of the strength of Santa Clara’s housing market.

Santa Clara County passed the milestone of its pre-recession employment peak in early 2013. Accounting for a population gain of just over 100,000 individuals in Santa Clara County since the 2008 recession, it finally reached a full jobs recovery for its population in Q1 2015.

In contrast, the state of California only just reached pre-recession employment numbers at the end of 2014 and didn’t reach a post-population-gain recovery until 2019.

Why did Santa Clara’s job market recovered more quickly than the rest of the state, and did it do the same following the 2020 recession? Roughly one-quarter of the jobs added since the recovery from the 2008 recession have been in the Professional-Business Services industry. This includes all of those tech industry jobs. The bulk of other new jobs can be found in industries that support the tech industry.

However, what was gained can be lost. The economic response to COVID-19 caused record job losses across the state, and the San Jose metro area was no exception. As of May 2023, Santa Clara County has just finally recovered, now 14,600 jobs above its December 2019 peak. But gains are leveling off quickly, expected to decrease later in 2023 with the recession. Any measure of job losses holds back regional economic growth and withholds stability from the housing market.

Employment by industry

While the 2020 recession caused job losses across all industries, jobs are gradually returning. In the construction industry, jobs are well over the pre-recession peak. Expect the construction industry to rise further in the coming years as construction starts rise in response to ever-present demand for local housing, spurred on by California legislative efforts to induce more residential construction.

Further, the number of real estate professionals is experiencing a rebound in 2023. However, expect the number of real estate professionals decrease to continue in 2023-2024, reflective of rising interest rates and decreasing sales volume.

Home sales volume impacts sales agent licensing and renewals

Income rising quickly

Chart update 05/11/22

Santa Clara County personal incomes are well above the statewide average. From 2019 to 2020, incomes increased 7.9% in the region, slightly less than the state average but still a significant increase.

But these income increases are not all that they seem. The high cost of housing in Santa Clara has pushed out many low- and moderate-income residents. So, while average incomes have increased in the region, the rapid pace of increase is at least partly due to many residents being forced to move to less costly areas.

Despite these relatively large income boosts, incomes actually need to increase much faster to meet the area’s rate of home price rise if home prices and rents are to be maintained. Home prices will need to fall in line with homebuyer incomes, especially as rising mortgage interest rates reduce buyer purchasing power in 2022. Along with global economic uncertainty and still-high home prices, homebuyer enthusiasm is beginning to be tempered by caution. Therefore, expect home prices to trend down in the second half of 2022 as we head into the next economic recession, forecasted to arrive by the end of 2023, bottoming in 2024.

Can the Fed cool inflation without implementing another recession?

About The Author

ft Editorial Staff

is the production staff comprised of legal editor Fred Crane, writer-editors Connor P. Wallmark, Carrie B. Reyes, Amy Platero, David Swistock, consulting instructor Summer Goralik, graphic designer Mary LaRochelle, video instructor Bill Mansfield and video editors John Rojas, Quinn Stevenson and Jose Melendez Avila.

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Despite limited inventory limiting sales volume, the Bay Area still seems to be heading for or going above the bubble zone.

Regardless of low inventory hurting sales volume, the Bay Area continues to look like it is approaching or extending into bubble territory.

I have renewed my license with First Tuesday . I started Real Estate in 1978.. I would like to ad First Tuesday to my face book friends and associates. I read all your real Estate updates and would like to share them. Wanda J Birdsong

Thank you for renewing with first tuesday ! If you would like to follow our Facebook page, you can find it here .

Regards, ft Editorial

I get it. You repost as new (see the only comment – mine from 6 months ago). The premises found in the first two paragraphs are even more incorrect now than back then.

1) Housing recovery not ‘on its way.’ It reached new peaks and continued upward as of two years ago. 2) If high cost is a drag on demand then I would hate to see the 100+ offers per property that might occur instead of the 10-20 we have now for the northwestern quadrant (the most expensive part of the Valley). Send one of your staff here and I’ll drive them around for a half day to demonstrate my points.

Low home sales volume is not due to cost. It is due to inventory. (Perceived) Low demand is not due to cost. It is due to inventory. The basic concept of supply and demand might be a little low-brow for this article but it works a lot better than the theories postulated here.

Steeply rising prices are not a drag on sales volume. A lack of inventory is a drag on sales volume. No one wants to sell when their investment is going up 15-20% per year (for the last 4 years). Most properties coming on the market at or above the median of $900K will receive 3-10 offers or more. Some cities have less than 3 weeks of inventory compared to 4.5 months nationwide. It will slow down and volume will pick up when appreciation settles back to 4-7%.

Silicon Valley (Santa Clara County and half of San Mateo County) is a destination. Most homebuyers do not have an exit plan. They plan to stay indefinitely. They have 25% to 50%+ down payment the majority of the time and are tolerant of market cycles. It is mostly built out despite stories of new construction. Those locations have 1-2 hour commute times (each direction) to major employers along the Peninsula.

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San Jose, CA Housing Market

The San Jose housing market is most competitive. Homes in San Jose receive 7 offers on average and sell in around 10 days. The median sale price of a home in San Jose was $1.4M last month, up 8.3% since last year. The median sale price per square foot in San Jose is $918, up 12.6% since last year.

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San Jose Housing Market Trends

In March 2024, San Jose home prices were up 8.3% compared to last year, selling for a median price of $1.4M. On average, homes in San Jose sell after 10 days on the market compared to 13 days last year. There were 499 homes sold in March this year, up from 471 last year.

How much is your home worth? Track your home’s value and compare it to nearby sales.

How hot is the San Jose housing market?

The Redfin Compete Score rates how competitive an area is on a scale of 0 to 100, where 100 is the most competitive.

Calculated over the last 3 months

• Most homes get multiple offers, often with waived contingencies.
• The average homes sell for about 8% above list price and go pending in around 9 days .
• Hot homes can sell for about 16% above list price and go pending in around 7 days .
• The average homes sell for about 9% above list price and go pending in around 8 days .
• Hot homes can sell for about 17% above list price and go pending in around 7 days .
• The average homes sell for about 12% above list price and go pending in around 9 days .
• Hot homes can sell for about 22% above list price and go pending in around 7 days .

San Jose Migration & Relocation Trends

• Across the nation, 3 % of homebuyers searched to move into San Jose from outside metros.
• New York homebuyers searched to move into San Jose more than any other metro followed by Santa Barbara and Pensacola .
• 78 % of San Jose homebuyers searched to stay within the San Jose metropolitan area.
• Sacramento was the most popular destination among San Jose homebuyers followed by Los Angeles and San Diego .

Home offer insights for San Jose

Recent offers.

19 offers total. Highest offer around $700k over asking

• Offer Not Accepted | 4 Weeks Ago ~ $1.25M Offer Sold for $1.5M — bd | — ba | ~ 1,500 sqft Over List Price 17% Days on Market — Competing Offers 10 Down Payment — Meena Jandir San Jose Redfin Agent
• Offer Not Accepted | 4 Weeks Ago ~ $1.75M Offer Sold for $2M 4 bd | 2 ba | ~ 1,500 sqft Over List Price 3% Days on Market 8 Competing Offers 8 Down Payment — Sadhana Vasudeva San Jose Redfin Agent
• Offer Not Accepted | 5 Weeks Ago ~ $1.25M Offer Sold for $1.5M 3 bd | 2 ba | ~ 1,000 sqft Over List Price 4% Days on Market 5 Competing Offers 11 Down Payment ~30% Michelle Huerta San Jose Redfin Agent
• Offer Not Accepted | 5 Weeks Ago ~ $1.25M Offer Sold for $1.25M 3 bd | 2.5 ba | ~ 1,750 sqft Over List Price 8% Days on Market 6 Competing Offers 9 Down Payment ~20% Ali Naimi San Jose Redfin Agent

View houses in San Jose that sold recently

Recently sold homes.

See all recently sold homes in San Jose .

San Jose top 10 neighborhood and home rankings

10 most walkable neighborhoods in San Jose

10 most transit-friendly neighborhoods in San Jose

10 most bikeable neighborhoods in San Jose

10 most affordable homes in San Jose

10 most expensive homes in San Jose

10 most popular homes in San Jose

Schools in San Jose

Greatschools rating, climate's impact on san jose housing.

Learn about natural hazards and environmental risks, such as floods, fires, wind, and heat that could impact homes in San Jose.

Flood Factor - Major

Flood factor.

Fire Factor - Major

Fire factor.

Wind Factor - Minimal

Wind factor.

Heat Factor - Major

Heat factor.

Transportation in San Jose

Walk score ®, transit score ®, bike score ®, more real estate resources for san jose, new listings in san jose.

• 144 S 3rd St #617
• 2633 Senter Creek Ct
• 1963 Tymn Way
• 373 Sposito Cir
• 1032 Curtner Ave
• 1476 Kooser Rd
• 1406 McQuesten Dr Unit D
• 253 El Portal Way
• 5343 Garrison Cir
• 1610 Heritage Bay Ct #6
• 992 Morse St
• 3013 Mauna Loa Ct
• 4850 Trent Dr
• 232 Belden Dr
• 313 Vista Roma Way
• All San Jose New Listings

Neighborhood Housing Markets

• Silver Creek Housing Market
• South San Jose Housing Market
• Santa Teresa Housing Market
• West Valley Housing Market
• North San Jose Housing Market
• Evergreen Housing Market
• Almaden Valley Housing Market
• Rose Garden Housing Market
• Berryessa Housing Market
• Willow Glen Housing Market

Zip Code Housing Markets

• 95120 Housing Market
• 95123 Housing Market
• 95124 Housing Market
• 95014 Housing Market
• 95125 Housing Market
• 95129 Housing Market
• 95131 Housing Market
• 95132 Housing Market
• 95135 Housing Market
• 95035 Housing Market
• 95148 Housing Market
• 94087 Housing Market
• 95117 Housing Market
• 95051 Housing Market
• 95118 Housing Market

Nearby City Housing Markets

• Sunnyvale Housing Market
• Alum Rock Housing Market
• Los Gatos Housing Market
• Cambrian Park Housing Market
• Saratoga Housing Market
• Lexington Hills Housing Market
• Santa Clara Housing Market
• Milpitas Housing Market
• Fremont Housing Market
• Cupertino Housing Market
• Morgan Hill Housing Market
• Campbell Housing Market
• Monte Sereno Housing Market
• East Foothills Housing Market
• Burbank Housing Market

Neighborhood Listings

• Silver Creek Real Estate
• South San Jose Real Estate
• Santa Teresa Real Estate
• West Valley Real Estate
• North San Jose Real Estate
• Evergreen Real Estate
• Almaden Valley Real Estate
• Rose Garden Real Estate
• Berryessa Real Estate
• Willow Glen Real Estate

Zip Code Listings

• 95120 Real Estate
• 95123 Real Estate
• 95124 Real Estate
• 95014 Real Estate
• 95125 Real Estate
• 95129 Real Estate
• 95131 Real Estate
• 95132 Real Estate
• 95135 Real Estate
• 95035 Real Estate
• 95148 Real Estate
• 94087 Real Estate
• 95117 Real Estate
• 95051 Real Estate
• 95118 Real Estate

Nearby City Listings

• Sunnyvale Real Estate
• Alum Rock Real Estate
• Los Gatos Real Estate
• Cambrian Park Real Estate
• Saratoga Real Estate
• Lexington Hills Real Estate
• Santa Clara Real Estate
• Milpitas Real Estate
• Fremont Real Estate
• Cupertino Real Estate
• Morgan Hill Real Estate
• Campbell Real Estate
• Monte Sereno Real Estate
• East Foothills Real Estate
• Burbank Real Estate
• Apartments for Rent in San Jose
• Houses for Rent in San Jose

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Affordable Housing Crisis: Only Six Major U.S. Cities Have Affordable Houses

T he affordable housing crisis in America is well-known, and recent data illustrates how widespread the problem is. A recent study by Clever Real Estate, which offers flat-rate buying services for real estate transactions, shows that 44 out of America's 50 largest Metropolitan areas don't have "affordable" housing prices for the median-earning household.

That means nearly 90% of America's largest cities have home prices too high for the average resident to afford. Housing is generally considered to be "affordable" when it does not cost more than 28% of the resident's monthly or annual earnings. Although America's major cities and suburbs have always had areas that were characterized by luxury homes or high wealth, they also had affordable areas where middle-class workers and wage earners could afford to live.

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• Only 0.2% of single-family rentals make this platform's rigorous cut. You can co-invest in them with Jeff Bezos and other notable investors for only $100 .
• Elon Musk, Ken Griffin and Jeff Bezos are bullish on one city that could dethrone New York and become the new financial capital of the US. Investing in its booming real estate market has never been more accessible . 

However, several consecutive decades of rising property prices and a lack of new home construction have conspired to limit inventory in most of America's major population centers. In what will come as no surprise to anyone who lives there, Los Angeles, California was rated the least affordable American city. LA residents need to earn roughly $250,000/year to afford the area's average home.

Notably, that's just the "average home" in Los Angeles, not a mansion in Beverly Hills or Pacific Palisades. Many Angelenos are paying nearly $1,000,000 for homes that don't even offer access to good public schools. That wasn't the case as recently as 15 years ago. Yes, homes were still expensive in the aftermath of the great financial crash, but interest rates were less than half of what they are now.

Rising interest rates have greatly reduced everyone's buying power, especially impacting entry-level and first-time buyers. Today's interest rates have made once affordable homes unattainable. It is also leading to a decrease in inventory as existing homeowners with low-interest mortgages choose not to sell.

• Want to Create a Passive Income Stream? These High-Yield Real Estate Notes Might Be Your Holy Grail  

It’s a tough squeeze for first-time homeowners. Treasury Secretary Janet Yellen recently lamented that it's getting "nearly impossible" to get deals done. The affordability issue extends beyond expensive cities like New York, San Francisco, and Los Angeles, as revealed by the Clever Study.

Americans are reacting the only way they know how: by moving to places where their buying dollar goes further. This is behind the rise of the Sunbelt and many other midwestern cities as destinations for Americans on the hunt for affordable housing. The six metropolitan areas listed as affordable in the Clover survey are:

1. Pittsburgh, Pennsylvania

2. Cleveland, Ohio

3. St. Louis, Missouri

4. Memphis, Tennessee

5. Indianapolis, Indiana

6. Birmingham, Alabama

Almost every city on that list has homes available for less than $300,000. In contrast, $300,000 could easily be a 20% down payment on a home in San Francisco or Boston's metropolitan areas. That explains why Zillow rated both Cleveland, Ohio, and Indianapolis, Indiana on its list of top 10 hottest housing markets of 2024. Birmingham, Alabama and Memphis, Tennessee are both prime Sunbelt markets that have been steadily growing in popularity with buyers.

After years of being "the" destination cities in America, places like New York, Los Angeles, and San Francisco are losing popularity due to sheer economics. If this trend continues, America's real estate landscape could look very different in just a few years.

• Commercial real estate has historically outperformed the stock market, but few investors have the capital or resources needed to invest in this asset class. This platform allows individuals to invest in commercial real estate with as little as $5,000. 
• This Bezos-backed startup selects America's prime single-family rentals and then lets you invest in them starting from $100. 

© 2024 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

This article Affordable Housing Crisis: Only Six Major U.S. Cities Have Affordable Houses originally appeared on Benzinga.com .