Abstract
The foreclosure crisis in the U.S. has resulted in a large number of residential REOs. These properties have been found to reduce the value of nearby homes. An unresolved issue is whether these negative spillover effects disappear after the REO is sold. We hypothesize that these effects are greater if the REO is purchased by an investor in comparison to an owner–occupant. In this paper we report the results from estimating the spillover effects of both current and ex–REOs, where the latter are divided into those possessed by owner–occupants and those possessed by investors. The results lend considerable support to our hypothesis.
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Notes
REO or Real Estate Owned is a class of property owned by a lender—typically a bank, government agency, or government loan insurer—after an unsuccessful sale at a foreclosure auction.
Wall Street Journal, Market Watch, February 28, 2013, “Foreclosure Sales and Short Sales Account for 43 % of U.S. Residential Sales in 2012 According to RealtyTrac,” www.marketwatch.com accessed on March 1, 2013.
CoreLogic, January 02, 2013, “CoreLogic Reports Shadow Inventory Continues Decline in October 2012,” www.corelogic.com accessed on March 1, 2013. CoreLogic estimates the pending supply of REOs for sale (aka the “shadow inventory”) by calculating the number of properties that are seriously delinquent, in foreclosure and held as REO by mortgage servicers but not currently listed on multiple listing services.
It should be noted that the finding in the existing literature that small investors purchase most REO properties could possibly be an artifact of coding error. The identification of investors in these papers relies on string matching to flag investor purchases and the number of properties that have been purchased by an investor. There are a number of limitations to this approach. First, properties can be held under different corporate names even if they are, for all intents and purposes, owned by the same entity. Second, the buyer and seller fields in transaction data are not generally standardized, so a person’s name may occur in several different formats within the database. For instance, if exact string matching is used, “John Doe,” “John A Doe,” and “John Do” will be three distinct buyers, and none of the properties are treated as investor-purchased. Alternatively, consider a case where a real estate investment group buys 50 properties: 5 of the properties are coded as being owned by “Realty Invest LLC,” 4 are coded as being owned by “Realty Investments LLC,” and 91 are coded as “Realty Investments." In this case, instead of identifying the fact that the properties are all owned by one large investor, an exact string matching algorithm would identify 2 small investors and one large investor as the purchasers of the REOs.
DataQuick classifies a sale as a short sale if the sales price is more than 5 % less than the estimated total loan balance at the time of the sale.
In Florida, after a lender files suit against a borrower for defaulting on a note, if the default is not cured, the obligor’s property is put up for sale at a public auction. At this auction, the lender is given a credit at the auction equal to the amount of the final judgment handed down by the court. A property then enters REO status if there is no other party that outbids the lender; such a situation will generally be categorized as a type–(1) transaction under the typology above. However, after reviewing a large number of the documents underlying the distressed sale field, we identified that in a non–trivial number of cases, type–(2) and type–(3) transactions also represented an entry into the REO stock, which is why we include such transactions in the universe of REO starts. Experimentation with other REO–start classification systems (e.g., using only type–(1) observations) revealed that our empirical results are largely insensitive to the inclusion of type–(2) and type–(3) REO starts in the REO stock; such a finding is consistent with a high repossession rate conditional on a property being put up for auction. For more on the foreclosure process in Florida, see: http://www.realtytrac.com/foreclosure-laws/florida-foreclosure-laws.asp
The 3–year limitation is imposed to guard against cases in which DataQuick’s distressed sale algorithm identifies an REO start but fails to identify the REO exit. In a small number of cases, a sale that is categorized as non–distressed occurs within the 3–year REO start window. In these cases, this non–distressed sale is coded as an REO exit.
It should be noted that our REO stock measure does not include properties for which foreclosure proceedings have commenced but have not yet gone to auction. Although such properties are certainly distressed, we have not included them in our stock measure because our data do not allow us to identify distress before the property is transferred at the foreclosure auction.
Models estimated with neighborhood–specific trend terms yielded similar results to those reported here.
At the suggestion of an anonymous referee, we also conducted two unreported robustness checks. First, we re-estimated all of the hedonic models using a sample where foreclosure sales and short sales were excluded. The coefficients on the hedonic parameters estimated using this subsample without distressed sales are very similar to those reported here. In the second robustness check, we stratified the sample in each county by tenure rate (i.e., the percentage of single-family homes that were owner-occupied) and re-estimated the models on each sub-sample. Specifically, for each county in our sample, we calculated the owner occupancy rate at the time of sale within 3000 ft of the transacting property; the median value of the homeownership rate was then used to partition the sample into low-homeownership (below-median homeownership rate) and high-homeownership (above-median homeownership rate) samples. For each county, separate regressions were then run for each of the subsamples. Comparing the coefficients across the different neighborhood types, the magnitude on the innermost REO term was generally slightly larger in low- as compared to high-homeownership neighborhoods. There did not appear to be a clear pattern in the differences in the coefficient sizes across neighborhoods for the other variables. Within each of the county-tenure rate subsamples, the results from the main analysis described in the text held. Namely, it was generally the case that REOs that were sold to owner-occupants produced smaller spillovers than properties in REO status, non-homesteaded ex-REO properties produced larger spillovers than homesteaded ex-REO properties, and the spillovers associated with non-homesteaded ex-REO properties declined with the amount of time since exiting from REO status.
The results obtained from using the square root model are nearly identical to those obtained from the levels model and are therefore not reported.
The standard deviation is computed separately for each ring within each county.
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Acknowledgments
The authors thank Kelley Pace, two anonymous referees, and participants in the 2013 FSU-UF Critical Issues in Real Estate Symposium for helpful comments. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Office of the Comptroller of the Currency or the U.S. Department of the Treasury.
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Ihlanfeldt, K., Mayock, T. The Impact of REO Sales on Neighborhoods and Their Residents. J Real Estate Finan Econ 53, 282–324 (2016). https://doi.org/10.1007/s11146-014-9465-0
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DOI: https://doi.org/10.1007/s11146-014-9465-0