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The Journal of Real Estate Finance and Economics

26.04.2024

Volume Traders of Non-Homogenous Assets

verfasst von: Darren K. Hayunga, Henry J. Munneke

Erschienen in: The Journal of Real Estate Finance and Economics

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Abstract

This article conducts a comprehensive analysis of the potential returns from investing in residential real property, with a special interest in any added gains due to trading larger volumes. Since the literature suggests that they are at a bargaining disadvantage, one notable finding is that individuals earn price benefits like so-called professional investors. Upon controlling for myriad well-known bargaining channels as well as investors’ demand characteristics, we find the mean price gains due to bargaining for individuals range from 4.2% for the lower volume investors (3–4 homes) to more than 7% for the highest volume traders (13 + properties). These average returns are for both buyers and sellers. Professionals exhibit bargaining acumen beginning with their first transactions (6.4%) and garner returns like individuals at higher volume levels (7.3%). The finding of investors’ bargaining effects generally increasing with greater volume implies gains in asymmetric information possibly through learning and/or reduced search costs.

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As in the capital markets, we define trading volume as the number assets traded during a given period. Our cumulative trading volume is measured over eight years, which is consistent with the literature investigating volume trading in housing. Mills et al. (2019) compute the number of homes purchased in one year. Allen et al. (2018) define investors using cumulative volume over a 4 year sample period. Bayer et al. (2020) use cumulative volume over a 17-year period.

The possible price determinants at this point in our modeling are ln(home age), ln(lot size), home size measured as ln(square feet), a binary variable for the presence of a pool, and almost 7,000 fixed effects measuring location at the subdivision level and time based upon month and year. The model exhibits a strong fit of transaction prices with an adjusted R² of 0.91.

Examples of overconfidence are found in security analysts, investment bankers, psychologists, physicians and nurses, engineers, and attorneys (Odean, 1998; Daniel et al., 1998; and citations therein).

Some of the most influential articles on overconfident investors include Kyle and Wang (1997), Odean (1998), Daniel et al. (1998), Odean (1999), Gervais and Odean (2001), Scheinkman and Xiong (2003), and Statman et al. (2006).

See also Haughwout et al. (2011), Lambie-Hanson et al. (2022), Garriga et al. (2019), Brunson (2020), Smith and Liu (2020), and D’Lima and Schultz (2022).

The institutional investors listed by Mills et al. (2019) are American Homes 4 Rent, American Residential Properties, Colony American Homes (a subsidiary of Colony Capital), Invitation Homes (a subsidiary of The Blackstone Group), Main Street Renewal (a subsidiary of Amherst Holdings LLC), Progress Residential, Silver Bay Realty Trust and Starwood Waypoint Residential Trust.

We also compare a sample of non-MLS homes to our test data based on the observable characteristics of bedroom and bathroom counts, living areas measured in square feet, and home ages. The non-MLS sample is constructed using the same applicable filters as our MLS sample. We find the summary statistics of non-MLS and MLS homes to be highly comparable, minimizing potential biases from their exclusion.

We also model bulk sales and the more general bulk transactions, the latter being the case when bulk purchasers buy from the same bulk sellers. They are not in our final equations as they are not influential price determinants and correlated with the volume bins.

While available in most public record databases, we do not include the number of bedrooms and bathrooms because they are collinear with the square feet measure and the parameter estimate on number of bedrooms often exhibits a negative slope when square feet is in the model. The negative slope is generally measuring an atypicality for the largest home, which we will measure directly using atypicality variables.

We use Kennedy (1981) for proper interpretation of a binary variable in a semilogarithmic equation.

As noted in the “Data Sample” section, we have assessed the inclusion of off-market trades in our volume counts. The absence of notable impact from these data on the findings presented in Table 6 can be provided by the authors upon request.

The symmetry assumption in Harding et al. (2003) pertains to the constraint that buyers and sellers with identical demographic traits place equal value on the traded good. This is not the same asymmetry we find between the purchase discounts and sale premiums using binary variables in, for example, Table 6.

A form of this idea is pocket listings, which have gained some exposure recently. See, for example, the article in the Wall Street Journal by Friedman (2021).

Medium volume for the listing agents is 24 to 102 transactions. Medium volume for selling agents is 13 to 38 transactions.

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Titel: Volume Traders of Non-Homogenous Assets
verfasst von: Darren K. Hayunga
Henry J. Munneke
Publikationsdatum: 26.04.2024
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-024-09986-z