SOS Community Stabilization Index

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Introduction

The SOS community stabilization index1 (SOS-CSI) is a composite index that aims to provide community leaders a relative measure of local housing market conditions, with a particular focus on recovery potential. The index is specific to conditions at the zip code level, and is comparable across all zip codes within a county or larger geographic area. Periodic recalculations of the index allow tracking relative changes in housing market conditions through time at the zip code level. The SOS-CSI synthesizes several variables into a single comparable measure of recovery potential. The benefit of this simplification, however, should not deemphasize the importance of using other available housing conditions measures, and understanding the limitations of this tool.

Data sources

The index draws data mainly from Lender Processing Services, Inc. Applied Analytics (LPS). The data include loan level servicing data for both securitized loans and loans held in portfolio from the top 10 residential mortgage servicers in the nation and others, covering about 60% of the US mortgage market. Smaller servicers have less representation. Also used are United States Postal Service (USPS) data on vacancies available from HUD, sales data by the Cuyahoga County Recorder, and census data on estimated housing units.

Components

The index is comprised of up to five weighted components calculated for each zip code:

1. REO inventory: This component represents the number of loans in Real Estate Owned (REO) status in the LPS data set, as of July 2009, weighted (multiplied) by the concentration of these REOs. For each zip code, the concentration of REOs is determined by dividing the number of REOs in the LPS data by the number of housing units according to the census. Only first lien loans on residential properties originated between 2005 and 2008 are included in the analysis.

2. 90 or more days delinquent: This component represents the number of loans that are at least 90 days delinquent or in foreclosure in the LPS data set as of July 2009, weighted (multiplied) by the concentration of these delinquent and foreclosed loans. For each zip code, the concentration of the 90-day-delinquent or foreclosed loans is determined by dividing these delinquent and foreclosed loans in the LPS data set by the number of housing units according to the census. Only first lien loans on residential properties originated between 2005 and 2008 are included in the analysis.

3. Median time of REOs on the market: This component represents the median length of time a REO property has been on the market. Both REO properties that have been sold and those that are still for sale are included in the calculation. This value is expected to be below the median time needed for REO properties to exit the market. Time on the market is based on LPS data. The LPS data provides the dates for when a property enters REO status and when a property exits REO status, if exit has taken place. For properties that have entered REO status but not yet exited, the current date is used in calculation. Only first lien loans on residential properties originated between 2005 and 2008 are included in the analysis.

4. Median home sales price decline: Median home sale prices are calculated or estimated for each zip code at each year from 2000 -2008. Differences between past (2000-2007) and current (2008) yearly median sales prices are computed and the highest decline is obtained. Those zip codes displaying no decline in sale prices are assigned a zero for this measure. The decline in the median yearly home sales price is weighted by the corresponding percentage decline. For communities with sales price data available, those data are used in this calculation. In Cuyahoga County, the sales transactions data are provided by the Cuyahoga County Recorder. For communities without access to these data, the appraisal amount from the LPS data is used. The appraisal amount reflects the sales price of the loan (for purchases) and the appraisal amount (for refinances). Only first lien purchase loans on residential properties originated between 2000 and 2008 are included in the analysis.

5. New originations-to-vacancy ratio: This component represents the number of originations inversely weighted by the rate of vacant properties. The ratio is calculated by dividing the number of new originations by the percent of vacant properties. The additive inverse of this component is used in the index so that, consistent with all previous components, higher values represent higher levels of distress. New originations are based on LPS data. Only first lien loans on residential properties originated in the first quarter of 2009 are included in the analysis. The vacancy data, generated by the United States Postal Service (USPS) and available from HUD, represents the average number of vacant residential properties over the first three months of 2009. The number of housing units used to calculate the percent vacant is available from the Census.

For each zip code, all components are normalized to a scale of zero to one based on the zip codes’ relative level of distress with respect to other zip codes in the county or larger geographic area. The composite index, a simple average of its components, is also normalized to a zero-to-one scale. A higher score on the index indicates a more distressed housing market with fewer signs of recovery. Zip codes with fewer than five REOs in the LPS database are excluded from the analysis.

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Interpretation

Interpretation: In general, greater potential for the local housing market and neighborhoods to recover may be observed via:

1. A decreasing influx of properties entering the foreclosure/REO process and an increasing outflow of properties from foreclosure/REO back into the market or into the hands of local institutions such as land banks.

2. Consumers’ positive expectations of stability in the area as signaled by lower depreciation of home prices and higher level of new mortgage originations relative to the number of vacant properties.

Components 1-3 of the index relate to the inflow-outflow perspective of recovery, while components 4 and 5 are in line with the positive expectations argument. Overall, the composite index aims to reflect the health of the overall housing market across zip codes, given the high negative impact due to the mortgage foreclosures crisis.

Figure 1

Maps

Following is a series of maps that, for each geography, depict the weighted index components individually and then as a composite SOS index (with both 4 and 5 components).

REO inventory

This component evaluates the number of REOs in each zip code as of July 2009, weighted by the concentration of these REOs (REOs per housing unit)

Figure 2

90-days and more delinquency

This component serves as a proxy for future REO influx. Similar to the REO availability index, this component evaluates the number of 90-day and longer delinquencies as of May 2009, weighted by the delinquencies per housing unit.

Figure 3

Median time of an REO on the market

This component evaluates the median length of time on the market for REO properties originated from 2005 through 2008. The component includes both the REO properties repurchased and currently for sale. A longer median time on the market usually suggests a more stagnant local housing market.

Figure 4

Median home sales price decline

This component evaluates the absolute decline of the median home sales price, weighted by the percentage of such decline, between 2000 and 2008. Lower levels of depreciation may suggest consumer expectations of stability in the area. The map on the left illustrates the index with the actual sales price while the map on the rights is based on an estimated sales price.

Figure 5

New originations to vacancy ratio

This component evaluates the recovery potential for the local housing market by examining new loan originations per vacancies. More originations with fewer vacancies may suggest a more stable housing market.

Figure 6

Community Stabilization Index for Cuyahoga County

The map illustrates the composite index of all five components.

Figure 7

Community Stabilization Index

The following map compares the SOS index to the NSP2 HUD index. It is important to notice that the former is a measure relative to other county zip codes, while the latter is calculated as an absolute measure. Scores of 18 and above are shown to be fairly consistent with the pattern outlined by the SOS index.

Figure 8

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Notes

  1. Adapted version of the Real Estate Owned (REO) Stabilization Opportunity Score (SOS) developed by Kai-Yan Lee at the Federal Reserve Bank of Boston [Return]