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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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Working Paper 17-02: Property Renovations and Their Impact on House Price Index Construction22933<h4 style="font-size&#58;13px;"><span aria-hidden="true"></span>​Abstract&#58;</h4><p>This paper provides the first wide-scale analysis of property renovation bias in repeat-sales house price indices across a multitude of U.S. geographies.&#160; Property improvements frequently lead to positive quality drift.&#160; In local markets, omitting information on property improvements can bias index estimates upwards.&#160; Bias often varies in a predictable manner and can distort valuations by as much as 15 percent in the central districts of large cities.&#160; This systematic variation in bias is partially a function of the disparate concentration of renovation activity with property improvements occurring more frequently in denser areas.&#160; The distortionary effect of not accounting for property renovations tends to decline outside of downtown areas and is generally negligible in smaller cities (populations below 500,000).</p>3/24/2017 8:16:56 PM236https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 17-01: The Daily Microstructure of the Housing Market21102<h3>Abstract&#58; </h3><p>The microstructure of the housing market includes periodic buyer liquidity constraints, high transaction costs, and bilateral negotiations on price and timing. These separately introduce daily price volatility and negative serial correlation that is suppressed at a monthly frequency. In a daily U.S. house price index, the annualized standard deviation of returns is 27 percent, versus 3 percent for monthly data. We attribute the daily volatility to repeating calendar-based liquidity price premiums (8 percentage points), transaction costs (7 pp), estimation and composition error (2 pp), and idiosyncratic shocks (10 pp). Monthly house price indices suggest housing has exceptionally high risk-adjusted returns. A daily index brings Sharpe ratios in line with other assets.<span aria-hidden="true"></span></p>2/2/2017 10:24:38 PM590https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 16-04: Missing the Mark: House Price Index Accuracy and Mortgage Credit Modeling21331<h4 style="font-size&#58;13px;">​Abstract&#58;</h4><p>We make two contributions to the study of house price index and mortgage credit modeling accuracy. First, we assess the predictive power of house price indices calculated at different levels of geographic aggregation. &#160;Lower levels of aggregation offer superior fit when appreciation rates vary substantially across submarkets and the indices are based on a sufficient number of transactions. Second, we estimate a competing options credit model using 15 years of mortgage performance data in the United States. Model accuracy is highest when using indices at a city or lower level of aggregation to construct current loan-to-value ratios. Fit is weaker when using state or national price indices. Overall, this research highlights the benefits of using more localized house price indices when predicting property values and mortgage performance.</p><p> <span style="line-height&#58;22px;"><font color="#444444">Please cite this working paper when using the annual HPI data for census tracts. Our other local HPI data were made for and described&#160;in our <a href="/papers/wp1601.aspx" target="_blank">Working Paper 16-01</a>.&#160;The local HPI data can be downloaded on the HPI Downloadable Data page or with these following links&#58;</font></span><br></p><ul><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_national.xlsx" target="_blank"><font color="#0072c6">National HPI</font></a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_state.xlsx" target="_blank"><font color="#0072c6">States</font></a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_cbsa.xlsx" target="_blank"><font color="#0072c6">CBSAs</font></a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_county.xlsx" target="_blank"><font color="#0072c6">Counties</font></a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_ZIP3.xlsx" target="_blank"><font color="#0072c6">Three-Digit ZIP Codes</font></a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_ZIP5.xlsx" target="_blank"><font color="#0072c6">Five-Digit ZIP Codes</font></a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_tract.csv" target="_blank"><font color="#0072c6">Census Tracts</font></a></span></div></li></ul><p> <em>Related papers&#58;</em>&#160;<a href="/papers/wp1601.aspx" target="_blank">FHFA Working Paper ​16-01&#58; Local House Price Dynamics​</a>&#160;and <a href="/papers/wp1602.aspx" target="_blank" style="line-height&#58;22px;font-family&#58;&quot;source sans pro&quot;, sans-serif;font-size&#58;14px;font-style&#58;normal;"> <font color="#0072c6">FHFA Working Paper ​16-02&#58; Local House Price Growth Accelerations</font></a><br></p>2/23/2017 1:55:45 PM1719https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 16-03: Oil Prices and Urban Housing Demand21225<h2>​​<span style="color&#58;#444444;line-height&#58;16px;font-family&#58;lato, sans-serif;font-size&#58;medium;"><em>Abstract&#58;</em></span></h2><p>​We develop a model of a monocentric, oil-exporting city. The model predicts a &quot;twist&quot;&#160;(rotation combined with a level shift) of the house price gradient with an oil price change due to the combined producer price and transportation cost effects. Using ZIP code level house price indices between 1975 and 2015, we show the slope of the house price gradient steepens in all cities when the price of oil is high and flattens when the price of oil is low. Areas specialized in oil and gas-related industries have house price changes that are positively linked with the price of oil. These results are consistent with theoretical predictions, and they quantify the large and differential risks to house prices associated with oil price changes both within and across all cities. Estimates suggest a 50 percent change in the price of oil results in a city-wide house price change of 20 percent over seven years in a city specialized in the production of oil (export employment share of 50 percent), whereas house prices for units greater than 15 miles from the city-center change in relative terms by -2 to -3 percent over the same period.</p><p><em>Related Paper&#58;</em>&#160;<a href="/papers/wp1601.aspx" target="_blank">FHFA Working Paper ​16-01&#58; Local House Price Dynamics​</a></p>2/2/2017 9:27:01 PM799https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 16-02: Local House Price Growth Accelerations20481<h4>​Abstract&#58;</h4><p>We document real house price growth accelerations in U.S. ZIP codes between 1975 and 2015. Acceleration episodes, which are defined to include relatively extreme periods of price growth, tend to exhibit temporal clustering and occur with greater frequency in large versus small cities. We exploit within-city variation in price dynamics to provide evidence that growth accelerations initially overshoot sustainable price levels but, in some areas, may reflect po​​sitive underlying economic fundamentals. Price levels post-acceleration are most sustainable in large cities, especially near city centers. Dynamics are generally consistent with empirical mean-reversion models and theories regarding the effects of traffic congestion and the elasticity of housing supply on house price gradients within the city.​</p><span style="line-height&#58;22px;"><font color="#444444"><span aria-hidden="true"></span>Please cite this our <a href="/papers/wp1601.aspx" target="_blank"><font color="#0072c6">Working Paper 16-01</font></a>&#160;when using the&#160;local HPI data. The local HPI data can be downloaded on the HPI Downloadable Data page or with these following links&#58;</font></span><br><ul><li><div style="font-style&#58;normal;"><span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_national.xlsx" target="_blank"><font color="#0072c6">National HPI</font></a></span></div></li><li><div style="font-style&#58;normal;"><span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_state.xlsx" target="_blank"><font color="#0072c6">States</font></a></span></div></li><li><div style="font-style&#58;normal;"><span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_cbsa.xlsx" target="_blank"><font color="#0072c6">CBSAs</font></a></span></div></li><li><div style="font-style&#58;normal;"><span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_county.xlsx" target="_blank"><font color="#0072c6">Counties</font></a></span></div></li><li><div style="font-style&#58;normal;"><span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_ZIP3.xlsx" target="_blank"><font color="#0072c6">Three-Digit ZIP Codes</font></a></span></div></li><li><div style="font-style&#58;normal;"><span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_ZIP5.xlsx" target="_blank"><font color="#0072c6">Five-Digit ZIP Codes</font></a></span></div></li><li><div style="font-style&#58;normal;"><span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_tract.csv" target="_blank"><font color="#0072c6">Census Tracts</font></a><span aria-hidden="true"></span></span></div></li></ul><p><em>Related paper&#58;</em> <a href="/papers/wp1601.aspx" target="_blank">FHFA Working Paper ​16-01&#58; Local House Price Dynamics​</a>​ and <a href="/papers/wp1604.aspx" target="_blank" style="line-height&#58;22px;font-family&#58;&quot;source sans pro&quot;, sans-serif;font-size&#58;14px;"><font color="#0072c6">FHFA Working Paper ​16-04&#58; Missing the Mark&#58; House Price Index Accuracy and Mortgage Credit Modeling</font></a></p>2/23/2017 1:55:43 PM1874https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 16-01: Local House Price Dynamics: New Indices and Stylized Facts19006<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 June 2016</span>​</span></h4>&#160;​ <h4 style="font-size&#58;13px;font-style&#58;normal;">​​Abstract&#58;</h4><p style="font-style&#58;normal;">W<span style="line-height&#58;22px;">e construct the first large-scale panel of annual house price indices for cities, counties, 3-digit ZIP codes, and 5-digit ZIP codes in the United States from 1975 through 2015 using source data with nea</span><span style="line-height&#58;22px;">rly 100 million transactions. Appreciation rates decrease with distance from the central business district (CBD) in large cities, suggesting an overall increase in the desirability of housing units in CBD locations and a general steepening of the house price gradient. Real house prices are more likely to be non-stationary near the CBD than in the suburbs, a finding consistent with a higher elasticity of housing supply near the edge of the city. Sustained real price increases and high price volatility near the centers of large cities suggest a lower supply elasticity in these locations.&#160;</span></p><p style="font-style&#58;normal;"> <span style="line-height&#58;22px;">The five-digit ZIP code&#160;HPIs constructed in this staff working paper are viewable in an <a href="/DataTools/Tools/Pages/HPI-ZIP5-Map.aspx" target="_blank">interactive map</a> under the &quot;Data &amp; Tools&quot; page. Our <a href="/PolicyProgramsResearch/Research/PaperDocuments/wp1601_FAQs_ZIP5_HPIs.pdf" target="_blank">FAQs</a> address common questions about the indices.&#160;Please cite this working paper when using the local HPI&#160;data.​ The local HPI data can be downloaded on the HPI Downloadable Data page or with these following links&#58;</span></p><ul><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_national.xlsx" target="_blank">National HPI</a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_state.xlsx" target="_blank">States</a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_cbsa.xlsx" target="_blank">CBSAs</a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_county.xlsx" target="_blank">Counties</a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_ZIP3.xlsx" target="_blank">Three-Digit ZIP Codes</a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_ZIP5.xlsx" target="_blank">Five-Digit ZIP Codes</a></span></div></li><li><div style="font-style&#58;normal;"> <span style="line-height&#58;22px;"><a href="/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_tract.csv" target="_blank">Census Tracts</a></span></div></li></ul><p style="font-style&#58;normal;"> <em style="color&#58;#404040;line-height&#58;22px;font-family&#58;&quot;source sans pro&quot;, sans-serif;font-size&#58;14px;font-weight&#58;normal;">Related papers&#58;</em><span style="line-height&#58;22px;font-style&#58;normal;">&#160;</span><a href="/papers/wp1602.aspx" target="_blank" style="line-height&#58;22px;font-family&#58;&quot;source sans pro&quot;, sans-serif;font-size&#58;14px;font-style&#58;normal;">FHFA Working Paper ​16-02&#58; Local House Price Growth Accelerations​</a><span style="line-height&#58;22px;font-style&#58;normal;">​</span>​ and&#160;<span style="line-height&#58;22px;">&#160;</span><a href="/papers/wp1604.aspx" target="_blank" style="line-height&#58;22px;font-family&#58;&quot;source sans pro&quot;, sans-serif;font-size&#58;14px;">FHFA Working Paper ​16-04&#58; Missing the Mark&#58; House Price Index Accuracy and Mortgage Credit Modeling</a><br></p>2/23/2017 1:55:44 PM10442https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
The Size of the Affordable Mortgage Market: 2015-2017 Enterprise Single-Family Housing Goals18381<p><span style="line-height&#58;1.6;">I</span><span style="line-height&#58;1.6;">n establishing benchmarks for the 2015, 2016, and 2017 single-family mortgage 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 paper documents the methodology used to estimate the market size for the Low-Income Borrower Home Purchase Housing Goal (share of borrowers with incomes no greater than 80 percent of the area median income (AMI)), the Very Low-Income Borrower Home Purchase Housing Goal (share of borrowers with incomes no greater than 50 percent of AMI), the Low-Income Areas Home Purchase Housing Subgoal (share of borrowers living in low-income areas (where census tract median income is no greater than 80 percent of AMI) and of borrowers with incomes no greater than AMI living in high minority areas), and the Low-Income Borrower Refinance Housing Goal (share of borrowers with incomes no greater than 80 percent of AMI).</span></p>8/19/2015 5:00:34 PM2550https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 15-3: Additional Market Risk Shocks: Prepayment Uncertainty and Option-Adjusted Spreads18211<h4 style="font-size&#58;13px;"></h4><span><span><h4 style="font-size&#58;13px;">​​Abstract&#58;</h4><p>Assessments of market risk for economic or regulatory capital typically involve calculating a portfolio’s sensitivity to key risk factor movements.&#160; Historically, practitioners have focused on two classical sources of risk, adverse changes in interest rates and volatility.&#160; As stress testing has evolved, additional risk factors have been identified, including several specific to fixed-income securities with embedded optionality.&#160; These include changes in prepayment rates or any of several other market risk factors, which affect option-adjusted spreads (OAS).&#160; We describe an empirical framework for generating shocks to prepayment rates and mortgage security OAS, which are consistent with simultaneous movements in other key risk factors, including the term structure of interest rates and implied volatility.&#160; Our prepayment rate shocks capture model misspecification and are calculated using historical performance data from multiple vendor prepayment models.&#160; These shocks are well defined, but capture only a portion of prepayment model error.&#160; Mortgage security OAS serves as a broader measure of model error, which encompasses both, model misspecification and forecasting errors as well as credit and liquidity risk.&#160; Our OAS shocks are calculated using historical six-month changes in spreads derived from multiple vendor quotes.​​</p></span></span><p><span style="font-style&#58;normal;">A revised version of this paper has been accepted for publication by the <em>Journal of Fixed Income</em> and can be accessed at <a href="http&#58;//www.iijournals.com/doi/abs/10.3905/jfi.2016.26.2.005" target="_blank">http&#58;//www.iijournals.com/doi/abs/10.3905/jfi.2016.26.2.005</a>.​​</span></p>10/4/2016 6:22:26 PM1580https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 15-2: The Marginal Effect of First-Time Homebuyer Status on Mortgage Default and Prepayment18135<p> <strong>Abstract&#58;</strong></p><p> This paper examines the loan performance of Fannie Mae and Freddie Mac first-time homebuyer mortgages originated from 1996 to 2012. First-time homebuyer mortgages generally perform worse than repeat homebuyer mortgages. But first-time homebuyers are younger and have lower credit scores, home equity, and income than repeat home-buyers, and therefore are comparatively less likely to withstand financial stress or take advantage of financial innovations available in the market. The distributional make-up of first-time homebuyers is different than that of repeat homebuyers in terms of many borrower, loan, and property characteristics that can be determined at the time of loan origination. Once these distributional differences are accounted for in an econometric model, there is virtually no difference between the average first-time and repeat home-buyers in their probabilities of mortgage default. Hence, the difference between the first-time and repeat homebuyer mortgage defaults can be attributed to the difference in the distributional make-up of the two groups and not to the premise that first-time homebuyers are an inherently riskier group. However, there appears to be an inherent difference in the prepayment probabilities of first-time and repeat homebuyers holding borrower, loan, and property characteristics constant. First-time homebuyers are less likely to prepay their mortgages compared to repeat homebuyers even after accounting for the distributional make-up of the two groups using information known at the time of loan origination.</p><p>&#160;</p>7/9/2015 2:00:09 PM2244https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 15-1: How Low Can House Prices Go? Estimating a Conservative Lower Bound17846<h4 style="font-size&#58;13px;">​​Abstract&#58;</h4><p>We develop a theoretically-based statistical technique to identify a conservative lower bound for house prices. &#160;Leveraging a model based upon consumer and investor incentives, we are able to explain the depth of housing market downturns at both the national and state level over a variety of market environments. &#160;This approach performs well in several historical back tests and has strong out-of-sample predictive ability. &#160;When back-tested, our estimation approach does not understate house price declines in any state over the 1987 to 2001 housing cycle and only understates declines in three states during the most recent financial crisis. &#160;This latter result is particularly noteworthy given that the post-2001 estimates are performed out-of-sample. Our measure of a conservative lower bound is attractive because it (1) provides a leading indicator of the severity of future downturns and (2) allows trough estimates to dynamically adjust as markets conditions change. &#160;This estimation technique could prove particularly helpful in measuring the credit risk associated with portfolios of mortgage assets as part of evaluating static stress tests or designing dynamic stress tests.​​</p><p>A revised version of this paper has been accepted for publication by the <em>Journal of Real Estate Finance</em> and Economics and can be accessed at <a href="http&#58;//dx.doi.org/10.1007/s11146-015-9538-8" target="_blank">http&#58;//dx.doi.org/10.1007/s11146-015-9538-8</a>. The research has been presented at the Federal Reserve Bank of Richmond, the American Real Estate Society annual meeting, and the American Real Estate and Urban Economics Association national conference​. Popular news coverage has included Calculated Risk, GARP, HousingWire, Inside Mortgage Finance, and RealtyTrac. It was also selected as best paper in 2015 in real estate cycles by the American Real Estate Society.<br></p>11/9/2015 2:30:57 PM10051https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx

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