The impact of current and former REOs across owner types: the case of Detroit

Abstract

This article examines home price spillovers associated with the number of nearby current and former real estate owned (REO) properties. The effect of active REOs is decomposed into the contributions of properties owned by the government sponsored enterprises (GSEs), US Department of Housing and Urban Development (HUD), and private entities to assess impacts associated with differences in managing REO inventories. The impact of former REOs is decomposed into spillovers associated with investor and owner-occupied properties. This study draws on home sale price data in the Detroit tri-county area 2008–2013. Results indicate REOs owned by HUD and private entities are associated with substantial discounts, with the largest effects appearing after remaining in REO inventory for more than one year. Investor-owned properties are associated with sustained and growing negative price effects through more than three years of ownership. Policies ensuring adequate oversight of REOs and sales to owner-occupants and non-speculative investors are encouraged.

Keywords:

• REO properties
• foreclosure
• spillovers
• housing prices
• Detroit

Acknowledgements

I would like to thank the anonymous referees for their valuable feedback. All errors remain my own.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 While Fannie and Freddie are not federal agencies, I refer to them at times as federal entities to reflect federal influence over these two corporations, particularly since conservatorship.

2 In many instances, Mortgage Electronic Registration Systems (MERS) is listed as sheriff’s deed buyer, but MERS only serves only as nominee, and it is therefore necessary to search subsequent records for the actual owner.

3 I identified and then exclude foreclosures repossessed by the US Department of Veterans Administration (VA), which are limited to home financed through the VA home loan program. These REOs are relatively few and including them as a variable does not change results.

4 I used regular expressions to classify buyers as investors where they matched a lengthy list of keywords and substrings including terms like “ACQUISITION,” “GLOBAL,” “INVESTMENT,” and “WEALTH.” I used a similar process to identify properties purchased by municipal and nonprofit entities, using terms like “AUTHORITY,” “CITY OF,” and “LAND BANK.” Complete lists are available from the author upon request.

5 For information about the number of REOs controlled by these different entities, their distribution in the Detroit tri-county area, and the share of properties sold to investors and owner-occupants, see Seymour (2020).