Building Home Price Indices: How and Why

In today’s uncertain real estate market, where home prices can fluctuate dramatically within months, understanding these changes is crucial for buyers, sellers, and policymakers alike. A home price index (HPI) is a vital tool for tracking these fluctuations over time. This essay explores the primary methods of constructing an HPI—median Automated Valuation Model (AVM) values, median observed sale prices, and repeat transaction index—each with its own advantages and challenges. By examining these methodologies, we can appreciate their pros and cons and develop a more nuanced understanding of their optimal applications.

1. Median Automated Valuation Model (AVM) Value

What is AVM?

An AVM is a technology-driven process that uses mathematical modeling to value properties. These models typically rely on data from various sources, including recent sales, tax assessments, and property characteristics, to estimate the current value of a home.

Methodology

To build an HPI using median AVM values, follow these steps:

1. Data Collection: Gather AVM estimates for a broad sample of homes within the target market or region.
2. Segmentation: Divide the data into appropriate segments based on geographic area, property type, and other relevant factors.
3. Calculation: Compute the median AVM value for each segment.
4. Index Construction: Use the median values to create the index, setting a base period (e.g., a specific year or quarter) to which all other periods will be compared.

Advantages

• Data Availability: AVMs provide extensive data coverage, often including properties that have not recently sold.
• Timeliness: AVM data can be updated frequently, allowing for real-time index updates.

Limitations

• Accuracy: AVM estimates may vary in accuracy and can be influenced by model assumptions and data quality.
• Lack of Actual Transactions: Relies on estimated values rather than actual sales prices.

2. Median Observed Sale Price

Methodology

Building an HPI using median observed sale prices involves the following steps:

1. Data Collection: Collect sale price data for all residential transactions in the target area over a specific period.
2. Segmentation: Segment the data by geographic area, property type, and other relevant criteria.
3. Calculation: Calculate the median sale price for each segment.
4. Index Construction: Develop the index based on the median sale prices, choosing a base period for comparison.

Advantages

• Reflects Market Activity: Uses actual sale prices, providing a direct reflection of market conditions.
• Simplicity: The calculation of median prices is straightforward and easy to understand.

Limitations

• Sample Bias: Can be influenced by the mix of properties sold in any given period (e.g., a higher proportion of luxury homes sold can skew the median price upward).
• Volatility: More susceptible to short-term market fluctuations.

Example

• NAR Index: Realtors prioritize potential earnings over purely economic metrics. This method is straightforward and effectively forecasts potential earnings for the next quarter. 

3. Repeat Transaction Index

Methodology

The repeat transaction index (RTI) method, popularized by the Case-Shiller Index, focuses on tracking price changes for the same properties over time. Here’s how it’s done:

1. Data Collection: Identify properties that have sold multiple times within the study period.
2. Paired Sales Analysis: For each property, calculate the price change between sales.
3. Adjustment for Time: Adjust the price changes for the time interval between sales to reflect the annualized rate of change.
4. Index Construction: Aggregate the adjusted price changes to build the index, again setting a base period for comparison.

Advantages

• Consistency: By tracking the same properties, it minimizes the effects of changes in the mix of sold properties.
• Accuracy: Provides a robust measure of true price appreciation or depreciation.

Limitations

• Data Requirements: A large dataset of properties with multiple sales is required, which may not be available in less active markets.
• Complexity: The methodology is more complex and computationally intensive than using median prices.

Examples

• Case Schiller Index: Among the very first HPIs to be produced, this is the technique employed by the Case-Schiller Index.
• FHFA Index: FHFA also uses this technique. It is very commonly used by “respectable institutions” because it is the oldest of the statistically well-justified techniques (though mean and median indices are, of course, older).

Hedonic Price Index

This method uses regression analysis to estimate the value of individual property characteristics (e.g., square footage, number of bedrooms). By holding these characteristics constant, it isolates price changes attributable to market conditions.

Advantages

• Detailed Insights: Provides information on the value of specific property features.
• Control for Quality: Adjusts for changes in property characteristics over time.

Limitations

• Data Intensity: Requires detailed data on property characteristics.
• Complexity: The regression models can be complex to implement and interpret.

Example

• Zillow HPI: Since Zillow already produces its Zestimate, this is a natural choice for them. It also assures that their HPI and their Zestimates are consistent. If Zillow used another technique, an anomaly could occur where the Zestimates are going down and an RTI is going up.  

Stratified Median Price Index

This method involves stratifying the market into homogeneous groups (e.g., by location, and property size) and calculating the median price within each group. The overall index is then constructed by aggregating these group medians.

Advantages

• Reduces Bias: Helps mitigate the impact of changes in the mix of properties sold.
• Simplicity: Easier to implement than some other methods.

Limitations

• Sample Size: Each stratum must have enough transactions to calculate reliable medians.
• Less Granular: This approach may miss nuances captured by more detailed models.

The Reasons to Build a Home Price Index

Home price indices (HPIs) are essential tools for economists, providing valuable insights into the housing market and broader economic conditions. These indices track changes in residential property values over time, serving various purposes in economic analysis, policy formulation, and investment strategy. By understanding how HPIs are constructed and utilized, economists can better gauge market trends, inform policy decisions, and support economic stability.

Economic Indicators and Monetary Policy

One of the primary uses of HPIs is to measure inflation and the cost of living. Housing costs represent a significant portion of household expenditures, making home price trends a critical factor in understanding overall inflation. HPIs are integral components of broader economic indicators such as the Consumer Price Index (CPI) and the Housing Price Index (HPI). By monitoring changes in home prices, economists can gauge inflation rates and make necessary adjustments to monetary policy.

Central banks, such as the Federal Reserve in the United States, use home price indices to inform their monetary policy decisions. By analyzing home price trends, central bankers can better understand inflationary pressures and adjust interest rates accordingly. For instance, rapidly rising home prices might prompt a central bank to increase interest rates to cool down the housing market and prevent a housing bubble. Conversely, declining home prices can signal economic downturns, prompting central banks to lower interest rates to stimulate the economy. Thus, HPIs play a crucial role in guiding interest rate decisions and ensuring economic stability.

Housing Market Analysis

Economists also use HPIs to analyze the dynamics of supply and demand in the housing market. By examining price trends, economists can identify periods of housing shortages or surpluses. This information is crucial for developers, policymakers, and investors to make informed decisions about housing supply and development strategies. Additionally, HPIs provide granular insights into regional and local housing markets. Economists can compare home price trends across different cities, states, or regions to understand local economic conditions, migration patterns, and the impact of local policies. This localized analysis helps in tailoring economic and housing policies to specific areas.

Financial Markets and Investment

Investors rely on home price indices to make informed decisions about real estate investments. By analyzing historical and current price trends, investors can identify lucrative markets, assess potential returns, and manage risks. HPIs help investors diversify their portfolios by understanding regional market variations. Furthermore, economists use HPIs to evaluate the mortgage market’s health and stability. Rising home prices can lead to increased mortgage origination while declining prices might result in higher default rates and foreclosures. By monitoring these trends, economists can assess the risk levels in the mortgage market and predict potential financial crises.

Public Policy and Social Impact

Home price indices also inform public policies related to affordable housing and urban development. Governments and policymakers use HPIs to design and implement affordable housing programs. By understanding price trends, they can identify areas with affordability issues and allocate resources to support low- and middle-income households. HPIs also help in evaluating the effectiveness of these programs over time. Additionally, HPIs inform urban planning and development strategies by highlighting areas of rapid price growth or decline. Economists use this information to advise on zoning regulations, infrastructure development, and land use policies. Understanding home price trends ensures that urban development aligns with economic and social goals.

Conclusion

Building a home price index involves selecting a methodology that best suits the available data and the specific goals of the index. The median AVM value method offers timely updates, the median observed sale price method provides a straightforward approach, and the repeat transaction index delivers consistency and accuracy. Each method has its strengths and challenges, and in practice, some organizations may use a combination of these approaches to create a more comprehensive index. Understanding these methods and their intricacies is crucial for anyone looking to build their first home price index.

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Franklin Carroll

Franklin Carroll is Kukun's VP of Modelling and Analytics; After finishing his studies at the University of Chicago, he worked at Fannie Mae where he helped build Collateral Underwriter, roll out the Value Verify initiative, and specialized in collateral related and credit related policy.

Published August 1, 2024