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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 18-03: Appraisal Accuracy, Automated Valuation Models, And Credit Modeling in Rural Areas25069<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>4/19/2018 6:00:16 PM291https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 18-02: House Price Markups and Mortgage Defaults24954<h4 style="font-size&#58;13px;"><span aria-hidden="true"><span aria-hidden="true"></span></span>​Abstract&#58;</h4><p>The transaction price of identical housing units can vary widely due to heterogeneity in buyer and seller preferences, appraisers, and search costs, generating &quot;markups&quot; above or below the average market price.&#160; These markups are mean reverting upon subsequent transactions, suggesting transitory factors play a role in same-unit dynamics. We show markups are an important driver of mortgage delinquencies, defaults, prepayments, and credit losses conditional on default. In general, our findings highlight several important aspects of mortgage risk management, including underwriting, insurance, and unit-level house value dynamics.</p>3/30/2018 5:07:42 PM810https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 18-01: Are Appraisal Management Companies Value-Adding? – Stylized Facts from AMC and Non-AMC Appraisals23655<p> <strong>Abstract&#58;</strong>​<br> In this paper, we study whether there are any systematic quality differences between appraisals associated and unassociated with appraisal management companies (AMCs). We find that compared to non-AMC appraisals, AMC appraisals on average share a similar degree of overvaluation despite being more prone to contract price confirmation and super-overvaluation. AMC appraisals also share a similar propensity for mistakes, despite employing a greater number of comparable properties. Our evaluation employs relatively simple statistical comparisons, but the results indicate no clear evidence of any systematic quality differences between appraisals associated and unassociated with AMCs.<br></p>3/26/2018 2:41:06 PM1710https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
NMDB Staff Working Paper 18-01: Mortgage Experiences of Rural Borrowers in the United States: Insights from the National Survey of Mortgage Originations24752<p>To date, research on rural mortgage markets in the United States has been limited by a lack of data on the specific mortgage experiences of borrowers living in rural areas.&#160; To fill this data gap, the National Survey of Mortgage Originations (NSMO) conducted a survey that oversampled people who took out mortgages in completely rural counties in 2014.&#160; This paper reports results from this survey, contrasting the characteristics, experiences, and loan terms of mortgage borrowers in completely rural counties to those of borrowers in metropolitan and other non-metropolitan areas.&#160; Completely rural counties are those with no urban cluster or an urban population less than 2,500.&#160; We find that borrowers in completely rural counties paid slightly higher interest rates on average and were less satisfied that their mortgage was the one with the best terms to fit their needs than borrowers in other areas.&#160; These results persist even after controlling for income, credit quality, and other borrower characteristics.&#160; Completely rural borrowers were less likely than other borrowers to be satisfied with the mortgage closing process, the timeliness of disclosures, and the disclosure documents themselves.&#160; Finally, compared with borrowers in more urban areas, borrowers in completely rural areas tend to be less confident or knowledgeable about some details of mortgages, and they are more likely to initiate contact with their lender.</p>3/14/2018 9:24:40 PM320https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
NMDB Staff Working Paper 18-02: First-Time Homebuyer Counseling and the Mortgage Selection Experience in the United States: Evidence from the National Survey of Mortgage Originations24753<p>​The existing literature on homebuyer education and counseling (HEC) consists almost exclusively of evaluations of specific programs, generally using mortgage loan performance as the metric of success.&#160; This paper contributes to the literature in two ways.&#160; First, it provides evidence on the benefits of HEC to mortgage borrowers in aspects other than mortgage performance.&#160; Second, the paper evaluates HEC in general, not just one specific program.&#160; It does so by drawing from a nationally representative sample of all first-time homebuyers in the United States who took out a mortgage in 2013 and 2014.&#160; The study data comes from the National Survey of Mortgage Originations (NSMO), a new survey co-sponsored by the Federal Housing Finance Agency (FHFA) and the Consumer Financial Protection Bureau (CFPB).&#160; We find that 14 percent of a nationally representative sample of first-time homebuyers reported receiving some form of HEC.&#160; Using two different matching estimation techniques (propensity score and coarsened exact matching) and ordinary least squares, we find that first-time homebuyers who reported receiving HEC also reported better mortgage knowledge, higher incidence of comparing final costs to the Good Faith Estimate (GFE), higher incidence of selecting a mortgage based on cost, and higher level of satisfaction with mortgage terms and the mortgage process.</p>3/14/2018 9:24:54 PM409https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
The Size of the Affordable Mortgage Market: 2018-2020 Enterprise Single-Family Housing Goals23139<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.&#160; FHFA has recently revised the market forecast models to include better specifications and new macroeconomic variables that reflect factors that impact the affordable housing market.&#160; This paper documents the statistical forecast models that the FHFA modeling team developed as part of the process for establishing the benchmark levels for the Enterprises for 2018 through 2020.</p>3/14/2018 8:53:59 PM1067https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
Working Paper 17-03: Under What Circumstances do First-time Homebuyers Overpay? – An Empirical Analysis Using Mortgage and Appraisal Data23060<h4 style="font-size&#58;13px;"><span aria-hidden="true"></span>​Abstract&#58;</h4><p>In this paper, we study whether first-time homebuyers overpay for their homes and whether the magnitude of overpayment varies with the diligence of appraisers involved. We investigate whether sales price renegotiation mitigates the overpayment problem and assess the connection between renegotiation and the characteristics of the appraisers involved in the underlying mortgage transactions. This research is among the first to contribute both theoretically and empirically to the existing literature on first-time homebuyers.</p>4/3/2017 7:00:27 PM769https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx
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><p>This research was selected as the best paper in 2017 in real estate valuation by the American Real Estate Society.</p>8/31/2017 5:06:07 PM1243https://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 PM1072https://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;">Our <a href="/PolicyProgramsResearch/Research/PaperDocuments/bdl_faqs_local_hpis.pdf" target="_blank"><font color="#0072c6">FAQs</font></a> address common questions about the indices. Please cite this working paper when using the local HPI data. The local HPI data can be downloaded on the HPI Downloadable Data page or with these following links&#58;</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">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><span style="line-height&#58;22px;"><span aria-hidden="true"></span><font color="#444444">This research was selected as the best paper in 2016 in real estate market analysis by the American Real Estate Society.</font></span></p><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;">FHFA Working Paper ​16-02&#58; Local House Price Growth Accelerations</a><br></p>3/13/2018 3:59:54 PM3927https://www.fhfa.gov/PolicyProgramsResearch/Research/Pages/Forms/AllItems.aspxhtmlFalseaspx

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