Poster Paper: The Impact of NSP on Bank Behavior

Saturday, November 5, 2016
Columbia Ballroom (Washington Hilton)

*Names in bold indicate Presenter

Brett R Barkley, Federal Reserve Bank of Cleveland

The Impact of NSP on Bank Behavior

Emre Ergungor[1], Lisa Nelson[2], and Brett Barkley[2]

April 4, 2016


The Neighborhood Stabilization Program (NSP), established in the wake of the 2007-2009 housing crisis, was intended to provide assistance to local jurisdictions primarily for the rehabilitation or demolition of distressed properties. We examine the impact of NSP2 in reducing the stock of distressed properties, with a focus on bank and investor activity.

Specific Research Questions

  Does nearby NSP2 investment:

  1. Increase a bank’s likelihood to list their real-estate-owned (REO) properties on multiple listing services (MLS)?
  2. Increase a bank’s likelihood to sell the listed property to an investor, non-profit, or individual buyer?
  3. Reduce liquidity costs (i.e., time property is on the market) incurred by banks?

To evaluate the impact of NSP in this way, we estimate a logistic model with fixed effects using a constructed panel dataset of real estate transactions at the parcel level. Data is from CoreLogic and is based on active MLS property listings, as well as historical data on foreclosure filings collected by CoreLogic from municipalities across the country. Data on properties receiving NSP2 funds is provided by HUD. Our analysis spans 2007 to 2015 starting three years before the implementation of NSP2 through the most recent year that data is available.

First, we construct a set of spatial weight variables to define the proximity of REO to NSP properties. Assume there is a market with two NSP properties j and k. For each REO property i in month we define

Sitd = 1/min(dij,dik)

Sit= ((1/dij)*N+ (1/dik)*Nk) / (1/dij + 1/dik)

Sit= ((1/dij)*W+ (1/dik)*Wk) / (1/dij + 1/dik)

where min(dij,dik) identifies the nearest NSP property to REO property i by distance in miles; Nj and Nk are the number of NSP properties within a 2-mile radius of NSP property j and k, respectively; and Wj and Wk are measures (not specified here) of the concentration of NSP properties within a 2-mile radius of NSP property j and k, respectively.

To examine how the allocation of NSP funds affects the overall stock of REO properties in an area, a logistic regression is estimated for

Yit = β1Sdit + β2SitN3Sit+ β5NSP2+ β7SitdNSP2t + β8Xb+ β9URct + αst+uit

where Yit is estimated for both listings and sales, which, in the first case, gives the likelihood of a bank listing an REO property for sale and, in the second case, gives the likelihood that the listed REO property is sold. The interaction terms, SitdNSP2t , is the variable of particular interest, which we would expect to be positive if NSP effectively stabilizes an area.

Preliminary results, only for Cuyahoga County, show that banks are more likely to list and sell their REO properties as a result of NSP2 investment. Additional analysis provides some evidence that investor purchases increased and bank liquidity costs decreased—however, these coefficients are not statistically significant.

[1] Research Department, Federal Reserve Bank of Cleveland

[2] Community Development Department, Federal Reserve Bank of Cleveland