Federal Housing Finance Agency Print

Research

​Research

 

Our economists conduct research on a range of topics in housing finance, including analyzing data and uncovering emerging trends.  In addition to presenting their research to policy makers, they share their research at academic conferences and publish in journals and other scholarly outlets.  Our work enables those interested in housing finance to make decisions based on the best information available.

In particular, our researchers focused on housing trends in house prices, housing market conditions, and mortgage lending activity.  In addition, we analyze the risk and capital adequacy of the housing government-sponsored enterprises and publish papers aimed at improving public understanding of the mortgage finance system.

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 Papers

 

 

The Size of the Affordable Mortgage Market: 2022-2024 Enterprise Single Family Housing Goals36164<p>In establishing benchmarks for the single-family home purchase housing goals for Fannie Mae and Freddie Mac (the Enterprises), the Federal Housing Finance Agency (FHFA) is required to measure the size of the mortgage market. This FHFA technical report documents the statistical forecast models that the modeling team has developed as part of the process for establishing the affordable housing goal benchmark levels for Fannie Mae and Freddie Mac for 2022 through 2024.<br><br></p>8/18/2021 3:08:43 PMHome / Policy, Programs & Research / Research / The Size of the Affordable Mortgage Market: 2022-2024 Enterprise Single Family Housing Goals Ken Lam, Omena Ubogu, Jay Schultz 815https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 19-02: A Quarter Century of Mortgage Risk26282<h4>Abstract&#58;</h4><font color="#000000" face="Times New Roman" size="3"></font><p>This paper provides a comprehensive account of the evolution of default risk for newly originated home mortgages over the past quarter century. We bring together several data sources to produce this history, including loan-level data for the entire Enterprise (Fannie Mae and Freddie Mac) book. We use these data to track a large number of loan characteristics and a summary measure of risk, the stressed default rate. Among the many results in the paper, we show that mortgage risk had already risen in the 1990s, planting seeds of the financial crisis well before the actual event. Our results also cast doubt on explanations of the crisis that focus on borrowers with low credit scores.</p><p>Note&#58; These indices are works in progress and all data, tables, figures, and other results in this working paper are subject to change. Earlier versions of this paper were posted in January, March (under the title “Mortgage Risk Since 1990.”), and October 2019. The May 2021 version adds refinance mortgages to the prior analysis which focused exclusively on purchase-money mortgages.&#160; Other improvements to the imputation and validation methods are also included.</p>5/20/2021 7:00:16 PMHome / Policy, Programs & Research / Research / Working Paper 19-02: A Quarter Century of Mortgage Risk William Larson (FHFA), Morris Davis (Rutgers), Stephen Oliner (American 12122https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 20-01: Land Valuation using Public Records and Kriging: Implications for Land versus Property Taxation in Cities27657<h4>Abstract&#58;</h4><font color="#000000" face="Times New Roman" size="3"> </font><p>We construct land values for each parcel in Maricopa County (Phoenix), Arizona, from 2000 through 2018 using a novel administrative dataset containing the universe of land sales and parcel records in the county. We then compare residential land values constructed using two classes of source data, vacant land sales and land under existing structures. Between 2012 and 2018, estimated land values for developed parcels are, on average, 14% higher when estimated using vacant land due to plattage effects and other unobserved factors. Growth rates are similar, facilitating the use of vacant land price indices to trace valuations over time from an accurate base year valuation. Dynamics between prices of Maricopa County land and housing suggest hypothetical land value tax revenues are more pro-cyclical than property tax revenues, with Betas with respect to national house prices of 3.3 and 2.3, respectively. By 2018, houses had recovered 96% of pre-crisis (2007) values, but land had only recovered 66%. These findings demonstrate a source of risk of dependence on public revenues from land value taxes versus a base-period revenue-neutral property tax.</p><font color="#000000" face="Times New Roman" size="3"> </font><font color="#000000" face="Times New Roman" size="3"> </font><p>Note&#58; An earlier version of this paper was posted in March 2020. The May 2021 update includes a refresh of external data sources used in the paper and refinements to some of the underlying methods.</p><font color="#000000" face="Times New Roman" size="3"> </font> <font color="#000000" face="Times New Roman" size="3"></font> 5/14/2021 2:00:40 PMHome / Policy, Programs & Research / Research / Working Paper 20-01: Land Valuation using Public Records and Kriging: Implications for Land versus Property Taxation in Cities 1929https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 21-01: Transaction Composition and House Price Index Measurement: Evidence from a Repeat-Sales Aggregation Index33182<h2>​Abstract&#58;<br></h2><p>We develop a framework for estimating city-level repeat-sales house price indices that are robust to submarket appreciation and sampling heterogeneity, two issues that confound classic approaches. A new algorithm ensures feasible estimation in all periods, despite cases of low transactions counts in some submarkets. With the geometric Laspeyres index as our target, numerical simulations show this algorithm uncovers the population index even when house prices, quantities, and transaction sampling vary across locations and over time. Then, using 40 million repeat-purchase transactions in the United States, we show differences exist between this index and transaction-weighted indices over certain periods and locations, especially in large cities.​<br></p>4/1/2021 5:02:05 PMWe develop a framework for estimating city-level repeat-sales house price indices that are robust to submarket appreciation and sampling heterogeneity, two issues that confound 1480https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 20-02: Nowcasting Unemployment Insurance Claims in the Time of COVID-1928208<font color="#000000" face="Times New Roman" size="3"> </font><h4>Abstract&#58; </h4><p>Near term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. Models including Google Trends are outperformed by alternative models in nearly all periods. Our results suggest that in times of structural change there is a bias-variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.</p><font color="#000000" face="Times New Roman" size="3"> </font>8/6/2020 8:12:46 PMHome / Policy, Programs & Research / Research / Working Paper 20-02: Nowcasting Unemployment Insurance Claims in the Time of COVID-19 Near term forecasts, also called nowcasts 2160https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
FHFA MORTGAGE ANALYTICS PLATFORM (Version 2.0)27705<p><strong>Release notes&#160;&#160;</strong></p><p>This version of the white paper incorporates the following two major updates to the FHFA Mortgage Analytics Platform&#58;</p><ol><li>Re-estimated performing loan equations for 30-year fixed rate, 15-year fixed rate, and 5/1 adjustable-rate mortgages.&#160; The re-estimated equations incorporated new explanatory variables important to predicting single-family mortgage delinquency.</li><li>Replaced the constant mortgage insurance (MI) haircuts with rating-based MI haircuts. </li></ol>5/21/2020 5:54:30 PMHome / Policy, Programs & Research / Research / FHFA MORTGAGE ANALYTICS PLATFORM (Version 2.0) White 2687https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 19-01: The Price of Residential Land for Counties, ZIP codes, and Census Tracts in the United States26065<h4>Abstract&#58;</h4><p>Data from millions of appraisals in 2012-2019 are used to estimate residential land prices, the share of house value attributable to land, and related statistics down to the census-tract level for areas that include the vast majority of U.S. population and single-family housing. The results confirm predictions about land prices from canonical urban models. Over 2012-2019, we show that land prices rose faster than house prices in large metro areas, boosting the land share of house value, while the land share fell in smaller metros. The data are available for download at <a href="/PolicyProgramsResearch/Research/Pages/wp1901.aspx">https&#58;//www.fhfa.gov/papers/wp1901.aspx</a>.</p><p>Note&#58; These indices are works in progress and all data, tables, figures, and other results in this working paper are subject to change.</p><p>&#160;</p>11/9/2020 3:27:28 PMWilliam Larson (FHFA), Jessica Shui (FHFA), Morris Davis (Rutgers), Stephen Oliner (AEI Data from millions of appraisals in 2012-2019 are used to estimate residential land prices 27036https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 18-04: A New Home Affordability Estimate 25655<h4 style="font-size&#58;13px;"> <span aria-hidden="true"><span aria-hidden="true"></span></span>​Abstract&#58;</h4><p>We offer a new home affordability estimate (HAE) that focuses on the share of housing stock that is affordable to certain households in the United States.&#160; The methodology considers affordability as it relates to funds available for down payments, initial monthly housing-related payments, and future projections of household income and costs.&#160; The HAE builds upon existing industry statistics in two ways.&#160; First, existing affordability indexes make certain assumptions for one or more of those funding factors.&#160; We can observe actual investment and expense values.&#160; Second, existing industry statistics consider “typical” families that earn the median household income level.&#160; The HAE is sufficiently more flexible for evaluating families at different places in the income distribution.&#160; This paper discusses the assumptions and processes for creating the HAE indexes; compares the national time series for very low-income, low-income, and median-income families; and then documents trends across metropolitan areas.&#160; We offer the data for public usage and leave commentary about implications to future research. </p><div>Please cite this working paper when using the HAE data which can be downloaded with these following links&#58;​<br></div><div><br></div><div><ul><li>​<span style="color&#58;#444444;font-family&#58;inherit;font-size&#58;inherit;font-weight&#58;inherit;"><a title="National HAE data" href="/DataTools/Downloads/Documents/Affordability/HAE_national.xlsx" target="_blank">National</a></span><br></li><li> <span style="color&#58;#444444;font-family&#58;inherit;font-size&#58;inherit;font-weight&#58;inherit;"><a title="MSA HAE data" href="/DataTools/Downloads/Documents/Affordability/HAE_metro.xlsx" target="_blank">Metropolitan Statistical Areas (MSAs)</a>​</span></li></ul></div><p> <br>&#160;</p><p> <br>&#160;</p>12/14/2018 7:55:20 PMHome / Policy, Programs & Research / Research / Working Paper 18-04: A New Home Affordability Estimate What Share of Housing Stock Can Families Afford 6473https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 18-03: Appraisal Accuracy and Automated Valuation Models in Rural Areas22974<p> <strong class="ms-rteFontSize-3">Abstract&#58;</strong></p><p>Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values. We then include these alternate value estimates when modeling delinquencies and examine their explanatory power. </p><p>A revised version of this paper has undergone external peer-review and is published in an academic journal with open (free) access. Citation&#58; Alexander N. Bogin, Jessica Shui. 2019. &quot;Appraisal Accuracy and Automated Valuation Models in Rural Areas.&quot; The Journal of Real Estate Finance and Economics, 1-13. <a href="https&#58;//link.springer.com/article/10.1007/s11146-019-09712-0" target="_blank">https&#58;//link.springer.com/article/10.1007%2Fs11146-019-09712-0</a></p>9/6/2019 2:47:19 PMHome / Policy, Programs & Research / Research / Working Paper 18-03: Appraisal Accuracy and Automated Valuation Models in Rural Areas N. Bogin, Senior Economist; Jessica Shui 2549https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 18-02: House Price Markups and Mortgage Defaults22973<h4 style="font-size&#58;13px;font-style&#58;normal;"> <span class="ms-rteThemeForeColor-2-0" style="line-height&#58;22px;font-style&#58;normal;"> <span style="line-height&#58;22px;font-style&#58;normal;">*Revised November 2020</span>​</span></h4>&#160;​ <h4 style="font-size&#58;13px;font-style&#58;normal;">​​Abstract&#58;</h4><p style="font-style&#58;normal;"><span style="line-height&#58;22px;">The transaction price of identical housing units can vary widely due to hete​rogeneity in buyer and seller preferences, matching, and search costs, generating what we term ``markups'' above or below the average market price. We measure markups for 3.4 million purchase-money mortgages and show they are an important driver of mortgage defaults and credit losses conditional on default even after accounting for collateral coverage (loan-to-value ratio) and a comprehensive set of other covariates. The findings suggest standard collateral coverage estimation may be inaccurate, with implications for both individual and portfolio-level credit risk assessment​.</span></p><p style="font-style&#58;normal;">This research was selected as the best paper in 2018 in real estate valuation by the American Real Estate Society.​<br></p>11/24/2020 4:23:23 PMHome / Policy, Programs & Research / Research / Working Paper 18-02: House Price Markups and Mortgage Defaults Paul E. Carrillo, George Washington University; William M. Doerner 4345https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx

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