Foreclosure crisis remediation in Lawrence, MA

Patricia Molina Costa

Master of Science (S.M.) candidate, 2011 Department of Urban Studies and Planning

Massachusetts Institute of Technology

11.520 GIS_Final Project

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Foreclosure crisis remediation in Lawrence, MA

Patricia Molina Costa, 11.520 GIS_Final Project

1. Background, Objectives and Hypothesis 2. Sources 3. Methods and Findings 4. Conclusion, Limitations and Future Research

1. Background, Objectives and Hypothesis

Lawrence, MA, is a small, post-industrial and immigrant city, 30 miles north of Boston. Although it is trying to overcome its industrial decline, it has recently been strongly hit by the foreclosure crisis, in part as a result of predatory lending practices by brokers in the city.

As a consequence, many properties have been foreclosed and are now Real Estate Owned (REO). This raises concerns about maintenance of the properties, problems with tenants that can´t reach the new owner of the house, etc. In order to make the new holders accountable, the City government passed the Ordinance #5-2008 in March 4, 2008, which establishes the obligation to register the property, provide a local contact person and maintain the property. Also, the City is interested in learning which properties are being foreclosed in order to make decisions about where to invest funds from the Neighborhood Stabilization Program (NSP).

The overall objective of this work is making a suitability analysis to target the potential areas were NSP funds should be invested by the city, based on density of REO properties and socioeconomic vulnerability.

For this project, my specific objective was to characterize the areas where REO properties are located, by testing the following hypothesis:

1. REOs tend to cluster

2. REOs locate in vulnerable areas, with:

− Low income − Low education levels − Low homeownership ratio − High percentage of minority population (Latinos in the case of Lawrence)

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2. Sources

The sources used for this project are the following:

The Citizens' Housing and Planning Association Database (CHAPA), which provides, among other data, the addresses of the foreclosed REO properties, the number of housing units per building foreclosed and the Year of Foreclosure.

The 2000 Census, SF3, from where I extracted the following data:

— Number of Housing Units (H1) — Median Income per Household (P53) — Education (P37) — Tenure H7 — Race (Hispanic or Latino by Race) P7

MassGIS website, where I found the Land Use Polygon Data from Essex County (2005)

Base Map: ESRI 9.3 Data & Maps

3. Methods and Findings

I used BatchGeo.com to get the Latitude and Longitude of the addresses of the foreclosed properties (149 points), as well as Google Maps for those properties that BatchGeo didn´t match. Then, I used AcMap (Tools > Add X, Y data) to create a layer with geocoded points.

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I then created a thematic map with the points classified by Year of Foreclosure:

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In order to avoid confusion in the analysis, I made an Intersection of the Residential areas with the Block Groups to find which Block groups should be excluded from the analysis, establishing as criterion those whose Residential area is less than 20% of Block Group.

Then I did a Spatial Join to find the density of REO units, using Sum to get the number of Units (not Buildings) and then using the Total number of units by block group from the census. I made a thematic map of Density of REOs and classified in 3 categories with quantiles: High, Medium and Low impact of foreclosed properties.

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To test my 1st hypothesis (if REOs cluster), I used Spatial Autocorrelation with Polygon Continuity. This ArcMap tool analyzes the proximity of polygons with similar values for a particular variable. It was ranked in the 4th position out of 5, 5 meaning Clustered and 1 meaning Dispersed. So it can be affirmed that REOs cluster in North Lawrence. To prove my 2nd hypothesis, I made thematic maps of the census variables that I was exploring, highlighting in red the block groups with high density of REOs to get a visual sense of the classification.

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As we can see, the high REO-density block groups have different values for each of the variables, and it is difficult to assess visually what is the general trend. Therefore, I decided to calculate the average of each variable for each of the 3 categories defined at the beginning: High, Medium and Low impact of foreclosures.

Foreclosure Impact

Average Median Income

Average %High

Education

Average %Home-owners

Average %Latino

Low 31.800 32,66% 39,39% 43,83%

Medium 27.007 27,20% 27,82% 68,77%

High 29.495 26,79% 30,63% 69,58%

The charts show a correlation in terms of Education, Homeownership and Race, meaning that for those areas with low impact of foreclosures, the percentage of high school graduates is higher than the areas with medium and high impact of foreclosures. The same happens with the ratio of homeownership, which is higher in less impacted areas. We also see that the most impacted areas are those with higher concentrations of Latino or Hispanic population.

However, we cannot see a correlation in terms of Median Household Income, as it is in medium-impact areas where we find lower incomes.

4. Conclusion, Limitations and Future Research

As a conclusion of this project, it can be said that REO properties tend to cluster in the northern part of Lawrence, and that there is a correlation in terms of Education, Homeownership and Race, but there is no correlation in Income.

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Ave_MedIncMedian Income ($)

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To test the vulnerability of these areas and their potential as targets for public investment, I should add more variables to the study, such as unemployment rates.

The next steps for a Site Suitability Analysis for NSP investment should include:

— Analysis of other vulnerability factors — Classification of REOs by Type of Owner, to target Publicly owned REOs (i.e. HUD, Fannie

Mae, Freddie Mac) — Analysis by year of foreclosure, in order to target those that have been standing as REO

for longer time.