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Impact of Investors in Distressed Housing Markets

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Abstract

We examine a recent trend in the market where investors purchase residential properties. We find that investors purchase at a discount of 9.5% compared to individuals purchasing in the same time period and geographic area. More specifically, we find that small investors purchase at a discount of 8.0%, medium investors purchase at a discount of 11.1%, large investors purchase at a discount of 13.6%, and institutional investors purchase at a discount of 7.7%. We also find that the presence of investor buyers in a market helps improve house values. A 10% increase in the percentage of houses purchased by investors in a census block leads to a 0.20% increase in house prices.

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Notes

  1. In 2012, Blackstone Group committed more than $3 billion purchasing and renovating single-family dwellings through its Invitation Homes division and related its subsidiaries. (http://www.blackstone.com/the-firm/overview/history, last accessed 5/6/2015.) Also, American Homes 4 Rent has acquired single-family dwellings in 30+ markets around the U.S. (http://americanhomes4rent.com/, last accessed 9/18/2013.)

  2. There is a large experimental literature on booms and busts in asset markets. In two recent studies involving experimental real estate markets, Ikromov and Yavas (2012a, 2012b), for instance, offer evidence that prices in experimental real estate markets deviate from their fundamental values, and the degree of these deviations are impacted by such factors as transaction costs, short selling restrictions, and the volatility of the cash flows from the asset.

  3. It is also worth noting two other studies that involved investor buyers. Gay (2015) examines the impact of real estate investors on housing affordability in the local market and finds a negative impact. Bracke (2016) studies purchases of buy-to-rent buyers and finds that they pay less than other buyers for equivalent properties.

  4. There is some possible ambiguity regarding the classification of small investors. We define small investor as 2 purchases or 1 purchase by a LLC, LP, etc. It is possible that some of these LLCs or LPs are individual buyers who simply set up an LLC or LP and purchase under that LLC or LP, in which case we would be overestimating the sample size of small investor group. As will be illustrated in our findings, this possible distinction is not of material importance. We analyze the role of each investor group, and the small investor group has the same directional and magnitudinal impact as other groups.

  5. All bulk sale 597 bulk properties (597 properties) by various investors were excluded from the sample due to price identification/assignment problems. They are identified as multiple parcel sales in the data. FDOR defines them as “Arm’s-length transaction transferring multiple parcels with multiple parcel identification numbers”. The 597 properties represent 0.82% of the sample right before we dropped them from the final Miami Dade County single family dataset. The rationale for removing them from the sample is that the sale price is the total of all properties and is the same for each bulk sale set of properties we examined. Thus, we are unable to determine a price for each individual property in these sets. One possibility is to divide price evenly across properties in a given bulk sale purchase, but this would not match the correct price with the correct characteristics of each property. As an illustration of the problem, for one bulk purchase by “Roar Investments” the mean, minimum, maximum and medium price is $1,098,000 for each of the 23 properties in this bulk sale set in 2011. We could divide the $1,098,000 by the total of 23 properties and assign this average price to each of the 23 properties, but that would assume that each of the properties are identical in terms of housing characteristics (square feet, age, pool, fireplace, etc.) and location. For the entire sample of 597 bulk sale properties the average bulk sale price is $1,108,393 with a median of 799,800.

  6. This definition is consistent with one provided by RealtyTrac (2015) where Institutional Investors/purchasers are entities that purchase at least 10 properties in a calendar year. We define our institutional investor group as having 10 or more purchases by a company in one of the five years in Miami-Dade County with all other purchases by these investors also classified as institutional purchases. This definition of Institutional Investor determines the maximum of the next group of large investor classification at 28 purchases. The choice of the cut for the other groups does not materially impact the empirical findings.

  7. See Schnure (2014) for a detailed analysis of the decline in home ownership rate and the rising role of institutional investors in single family rental homes.

  8. See Camargo et al. (2014) for a discussion of how the information contained in asset prices plays a crucial role in the decision-making processes of many agents in the economy and what role a government can play in “unfreezing” a market. Cespa and Foucault (2014) show how a small drop in the liquidity of one asset can propagate to other assets and can, through a feedback loop, lead to a large drop in market liquidity and to flash crashes.

  9. See, for example, Houghwout et al. (2011) and Bayer et al. (2015) for the role of speculative investor buyers in the recent rise and crash in the housing market.

  10. The term “buyer power” should not be confused with “buying power” which is commonly used to refer to the amount of money available to purchase a good or service.

  11. See von Ungern-Sternberg (1996) for a detailed exposition of the theory of countervailing power.

  12. The rationale for the time period is that grantor and grantee information is available from January 2009 and we extracted the data in September/October 2013.

  13. See “Real Property Transfer Qualification Codes for use by DOR & Property Appraisers Beginning January 1, 2012” at: http://dor.myflorida.com/dor/property/rp/dataformats/pdf/salequalcodes12.pdf

  14. This may result in a bias toward zero with regards to the size and significance of the coefficient on the cash variable in the regression models. The overall cash percentage is 43.48%. The cash percentage is approximately 41% for the MLS sales and the estimated cash percentage is approximately 47% for the non-MLS sales using this method. These numbers are consistent with the 43% estimate by RealtyTrac, August 29, 2013 (http://www.inman.com/2013/08/29/all-cash-deals-on-the-rise/).

  15. This definition is consistent with one provided by RealtyTrac (2015) where Institutional Investors/purchasers are entities that purchase at least 10 properties in a calendar year.

  16. Note that we ended the sample in September 2013 when we collected the data, thus we are not comparing a full year of data to prior years.

  17. For example, a report by Goldman Sachs, in the Mortgage Analyst, August 14, 2013 titled “How much upside to purchase mortgage originations?” estimates an increasing percentage of cash transactions with approximately 30% cash transactions in 2009 and roughly 58% in the summer of 2013. RealtyTrac, August 18, 2014 state: “Among metropolitan statistical areas with a population of at least 500,000, those with the top six highest percentages of cash sales were all in Florida: Miami-Fort Lauderdale-Pompano Beach (64.1%)” is the highest.

  18. The trend of increasing cash transactions and decreasing MLS market share is interesting. Banks are generating fewer transactions, impacting the fees they earn from financing residential real estate and brokers are selling a lower percentage of the transacting properties resulting in lower demand for real estate broker services and most likely lower total dollar commissions for real estate brokers. This analysis is admittedly limited to one market, so it would be interesting to see if this trend is occurring in other markets nationally.

  19. Note that public records do not include information on marketing time. Therefore, analysis of marketing time is limited to properties sold thru the MLS.

  20. We do not have time-on-the-market data for non-MLS properties. MLS sales comprise approximately 64% of the full sample.

  21. In Table 7, we define small investor as 2 purchases or 1 purchase by a LLC, LP, etc. It is possible that some of these LLCs or LPs are individual buyers who simply set up an LLC or LP and purchase under that LLC or LP, in which case we would be overestimating the sample size of small investor group. In unreported results, we redid the analysis in Table 7 by redefining small investor as at least two purchases. Although this led to a small decrease in discounts enjoyed by each investor group, the results are similar to those reported in Table 7. We also extended the analysis to separate investors into flippers and non-flippers, where a flipper purchase is defined as an investor purchase that resold within two years of the original purchase. We find that flipper investors enjoy a discount of 15%, more than non-flipper investors. While controlling for flipper investors decreases the discount enjoyed by each investor group marginally, the results remain similar to those reported in Table 7.

  22. In order to party reduce informational disadvantage they were facing, many institutional investors, including industry leader Blackstone, started partnering with smaller firms by 2012, who could provide better knowledge of local markets (Gittelsohn 2012).

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Authors and Affiliations

  1. Alico Eminent Scholar, Department of Economics & Finance, Lutgert College of Business, Florida Gulf Coast University, Fort Myers, FL, 33965, USA

    Marcus T. Allen

  2. Department of Finance, University of South Florida, Tampa, FL, 33620-5500, USA

    Jessica Rutherford

  3. Bank of America Endowed Professorship, College of Business, Finance Department, University of South Florida, University of South Florida, Tampa, FL, 33620-5500, USA

    Ronald Rutherford

  4. Robert E Wangard Chair, School of Business, University of Wisconsin – Madison, 5251 Grainger Hall, 975 University Avenue, Madison, WI, 53706, USA

    Abdullah Yavas

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  1. Marcus T. Allen
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  2. Jessica Rutherford
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  4. Abdullah Yavas
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Correspondence to Ronald Rutherford.

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Allen, M.T., Rutherford, J., Rutherford, R. et al. Impact of Investors in Distressed Housing Markets. J Real Estate Finan Econ 56, 622–652 (2018). https://doi.org/10.1007/s11146-017-9609-0

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