Fannie Mae commissioned this paper in connection with its 2021 Duty to Serve plan. This paper was prepared by May 8 Consulting, Inc., and 

Reinvestment Fund. While Fannie Mae consulted with the authors, and reviewed the paper’s content, the conclusions presented are solely those 

of the authors.

© 2021 Fannie Mae

Evidence-Based 

Policy Making 

An Analysis of Six Strategies to Stabilize Neighborhoods

By: May 8 Consulting and Reinvestment Fund

2021
© 2021 Fannie Mae Page 2 of 60

Contents

Executive Summary ..................................................................................................................................................................................3

Headline Findings.................................................................................................................................................................................7

Introduction ............................................................................................................................................................................................10

Methodology ...........................................................................................................................................................................................13

Research Findings: Basic Policy Analysis of Six Policy Interventions ...................................................................................................16

Code Enforcement............................................................................................................................................................................16

Demolition .........................................................................................................................................................................................22

Scattered Site Rehabilitation..........................................................................................................................................................30

Land Bank...........................................................................................................................................................................................36

First Look Program.............................................................................................................................................................................42

Emergency Bridge Loan/Soft Second Mortgage Program ................................................................................................................47

Conclusion...............................................................................................................................................................................................52
© 2021 Fannie Mae Page 3 of 60

Executive Summary

For each of the interventions, the team selected two local programs that utilized the intervention and were willing to share data 

for analysis. 

In order to provide practitioners with evidence-based guidance in real time as they define a local response to the COVID-19

economic crisis, the Fannie Mae team adopted a quick, basic policy analysis approach. That approach to research, best described 

by Patton, et al. (2013), is work that is needed in situations when policy analysts are called on “to give advice to policy makers in 

incredibly short periods of time, in contrast to university researchers and think tank consultants who are hired specifically to 

conduct intensive research on public policy issues.” Quick, basic policy analysis is uniquely appropriate in a moment when 

policymakers and practitioners desire to make rapid but data-based decisions that have a reasonable prospect to address a 

problem they confront. It is important to note that quick policy analysis is not robust enough to definitively identify causal links 

between interventions and neighborhood stability nor was the team able to determine statistical significance for correlations or 

associations due to the small sample size. Issues of selection bias further constrain the ability to draw conclusions; areas may 

choose to implement a program for specific reasons that we cannot observe and which may be correlated with stabilization. As a 

result of the limitations, the findings for any of the six interventions studied cannot be generalized to all local instances of that 

intervention. What these analyses provide is important information about changing stability over time within particular 

neighborhood settings that were “treated” by a particular version of each intervention tool. Details about each program and 

neighborhood context can help policymakers tailor any of the interventions to their own local resources, market conditions, and 

policy goals.

As local practitioners begin to assess 

strategies to address the economic 

impacts of the COVID-19 pandemic on 

their communities, this report seeks 

to provide information that will be 

helpful in their efforts to effectively 

and equitably protect and improve

housing market conditions in lowincome and middle-income

neighborhoods. 

The research team, made up of May 8 

Consulting, Inc., and Reinvestment Fund (Fannie 

Mae team), performed a basic policy analysis of 

the effectiveness of six common tools that urban 

and suburban local governments and their 

partners use to stabilize low-income and middle 

neighborhoods. This effort paid particular 

attention to tools that are being used to stabilize 

or revitalize housing markets in neighborhoods 

of color that have been destabilized and 

weakened by historically racist policies like 

redlining, race-based zoning ordinances, and 

restrictive covenants.

The policy interventions selected for analysis are:

Code Enforcement – System of inspections, fines, and 

fees to enforce private property condition standards;

Demolition – Removal of deteriorated or dangerous 

buildings to eliminate blighting conditions;

Scattered Site Rehabilitation – Acquisition and 

rehabilitation of vacant single-family homes for sale or rental;

Land Banks – Government agency or nonprofit established 

entities charged with reactivating vacant and underutilized 

properties and returning them to productive use;

First LookTM Programs – Programs that provide owner 

occupants and local nonprofit buyers with an exclusive “first 

look” to purchase single-family real-estate-owned (REO) 

properties in the portfolios of financial institutions; and

Emergency Bridge Loan/Soft Second Mortgage 

Programs – Short-term loan programs to prevent 

foreclosure for homeowners who are delinquent on 

mortgage payments.

1

2

3

4

5

6
© 2021 Fannie Mae Page 4 of 60

Table 1: Policy Interventions Summary Table

Policy Intervention Program Analyzed -

Name

Program 

Administrator 

(Type and Name)

Program 

Geography

Metrics Indicative 

of Stabilization 

Analyzed

Program Years 

Analyzed 

Code Enforcement

Streamlined Code 

Enforcement Program

Govt: City 

Department of 

Housing and 

Community 

Development

Baltimore, MD Sale Price, Vacancy 2010-2020

All Together Now 

Program

Govt: Department of 

Regulatory Services

Minneapolis, MN Sale Price, Nuisance 

Violations

2014-2019

Demolition

Dangerous Buildings 

Initiative

Govt: Neighborhoods 

and Housing Services 

Department 

Kansas City, MO Sale Price, Vacancy, 

Tenure

2016-2018

City County 

Demolition Project

Govt: Department of 

Regulatory Services

Hennepin County, 

MN

Sale Price, Sale 

Volume, Vacancy

2009-2013

Scattered Site 

Rehabilitation

West Philadelphia 

Scattered Site Model

For Profit: WPRE and 

Neighborhood 

Restorations

Philadelphia, PA Sale Price, Vacancy, 

Foreclosure Filings

2010-2014

Atlanta Neighborhood 

Development 

Partnership Scattered 

Site Model

Nonprofit: Atlanta 

Neighborhood 

Development 

Partnership

DeKalb County, GA Sale Price, Sale 

Volume, Assessed 

Value, Foreclosure 

Filings, Tenure

2010-2014

Land Bank

Houston Land Bank Govt: Housing and 

Community 

Development 

Department

Houston, TX Sale Price 2010-2019

Albany County Land 

Bank

Nonprofit: Albany 

County Land Bank

Albany, NY Sale Price, Vacancy 2015-2020

First Look Program

New Jersey 

Community Capital's 

Community Asset 

Preservation 

Corporation

Nonprofit: New 

Jersey Community 

Capital

Essex County, NJ Sale Price, Sale 

Volume, Bank Sales

2013-2016

Community 

Investment 

Corporation 

Community Initiatives 

Acquisition Program 

Nonprofit: 

Community 

Investment Corp

Cook County, IL Sale Price, Sale 

Volume, Completed 

Foreclosure

2013-2016

Emergency Bridge 

Loan

Homeowners’ 

Emergency Mortgage 

Assistance Program

Govt: Pennsylvania 

Housing Finance 

Agency

Pennsylvania/ 

Analysis of 

Allegheny County 

Sale Price, Sale 

Volume, Foreclosure 

Filings

2010-2014

Delaware Emergency 

Mortgage Assistance 

Program

Govt: Delaware State 

Housing Authority

Delaware/ Analysis 

of Wilmington 

Sale Price, Sale 

Volume, Foreclosure 

Filings

2011-2019
© 2021 Fannie Mae Page 5 of 60

Because neighborhood housing markets are complex and fluid, and no single indicator can fully capture their relative stability 

and direction, the team evaluated multiple metrics for each intervention where available. Metrics were selected based on their 

relevance to stabilizing low-income and middle neighborhood markets

. As with the selection of interventions for study, the metrics were chosen to focus on market characteristics most relevant to 

homeowner stability. Due to variations in local data availability (e.g., in Houston, foreclosure filings are not publicly available, and 

the quality of vacancy data varies widely by location and source), the team was not able to use a uniform set of indicators across 

all interventions. Nor could the team always access preferred indicators for certain interventions; the team sought metric data for 

periods immediately before and after the intervention time period, but in some cases, the available data overlapped with 

program activities. Additionally, because this is an exploratory 

analysis of programs with a wide variety of goals, scales of 

intervention, and neighborhood conditions, the team did not define 

success using a threshold value for any of the indicators (e.g., a 25% 

increase in median sale price or a 10% decline vacancy). The most 

common metrics used to define whether the neighborhood 

experienced stabilization were: 

 Home sale prices 

 Home sale volume

 Foreclosure volume and rate

 Residential vacancy

Sale prices are an indicator of whether existing low- and moderate-income owners are maintaining equity in their homes and able 

to access financing or might be at risk of going underwater on their mortgage and whether there is buyer confidence in the local 

market. Low sales volume can indicate market stagnation and potential disinvestment. Foreclosures are a more direct measure of 

stability and forced moves. Residential vacancy is a commonly used measure of disinvestment—and the potential loss of housing 

units due to inhabitability or abandonment. 

It is important to note, however, that in the community development field, there is often a tension between investment and 

displacement, and housing market dynamics reflected in the selected metrics may have both positive and negative impacts 

based on local context and resident characteristics. For example, rising sale prices may be followed by rising property taxes that 

can strain lower income homeowners who are not eligible for senior tax freezes or other programs and may put ownership out of

reach for first time buyers. Sales volume could be reflective of short or distressed owner sales. Falling vacancy may make it 

difficult for some home seekers to find affordable units. Renters tend to be more vulnerable to fluctuations in market strength, as 

are all households that have less wealth. As a result, some signals of market stabilization and strength may also raise concerns 

about gentrification.

Although stabilization was the focus of this report, the team endeavored to address potential concerns regarding gentrification by 

assessing whether market changes likely indicated stability for low or middle-income residents, or a demographic transformation 

reflecting the replacement of one group of residents with another, higher-income group. Though definitions of gentrification vary, 

the term generally refers to the arrival of higher income households and the departure of lower income ones in a neighborhood, 

sometimes accompanied by racial change.1 Appendix A presents a series of data tables documenting changes in income 

racial/ethnic makeup and income in all 12 intervention locations. While many of the neighborhoods evaluated saw growth in 

sales price during the study period, these tables show there were not substantial increases in incomes—many remained below 

their metropolitan median income—nor were there shifts from non-white to white populations.

1

See, e.g., Hwang, J., & Sampson, R. J. (2014). Divergent pathways of gentrification: Racial inequality and the social order of 

renewal in Chicago neighborhoods. American Sociological Review, 79(4), 726-751; Kirkland, E. (2008). What's Race Got to Do With 

it? Looking for the Racial Dimensions of Gentrification. Western Journal of Black Studies, 32(2); Chronopoulos, T. “What’s 

Happened to the People?” Gentrification and Racial Segregation in Brooklyn. J Afr Am St 24, 549–572 (2020). 

https://doi.org/10.1007/s12111-020-09499-y; Jonathan Jackson (2015) The Consequences of Gentrification for Racial Change in 

Washington, DC, Housing Policy Debate, 25:2, 353-373, DOI: 10.1080/10511482.2014.921221

Sale prices are an indicator of whether 

existing low- and moderate-income 

owners are maintaining equity in their 

homes and are able to access financing 

or might be at risk of going underwater 

on their mortgage and whether there is 

buyer confidence in the local market.
© 2021 Fannie Mae Page 6 of 60

The Fannie Mae team evaluated programs that were implemented in either low-income areas (where median incomes ranged 

from 30%-60% of the local metro area median according to ACS estimates for the analysis starting year for that intervention) or 

middle neighborhoods (where median incomes ranged from about 70% to 130% of the local metro area median in the analysis 

starting year).

Table 2: Program Neighborhood Income and Density Type

Policy Intervention Program Geography

Median Treated Area 

Income as Share of Local 

Metro Median Income

Neighborhood 

Income Type

Neighborhood 

Density Type

Code Enforcement

Baltimore, MD 68% Middle Urban

Minneapolis, MN 128% Middle Urban

Demolition

Kansas City, MO 42% Low Income Urban

Hennepin County, MN 54% Low Income Urban

Scattered Site 

Rehabilitation 

Philadelphia, PA 31% Low Income Urban

DeKalb County, GA 110% Middle Suburban

Land Bank

Houston, TX 39% Low Income Urban

Albany, NY 52% Low Income Urban

First Look Program

Essex County, NJ 82% Middle Urban/Suburban

Cook County, IL 94% Middle Urban/Suburban

Emergency Bridge 

Loan

Allegheny County, PA 129% Middle Suburban

Wilmington, DE 99% Middle Urban 
© 2021 Fannie Mae Page 7 of 60

Headline Findings

Code Enforcement

 Streamlined Code Enforcement Program (SCEN), Baltimore, MD – Proactive code enforcement in middle 

neighborhoods targeted at vacant buildings and lots.

 All Together Now Program (ATN), Minneapolis, MN – Engagement of residents and neighborhood associations prior to 

proactive inspections.

Summary of Findings: These two programs were the most geographically targeted of all the programs evaluated and showed the 

strongest association between program activity and neighborhood stabilization. There was observable improvement and 

relatively more stability in middle markets that were treated by Baltimore’s Streamlined Code Enforcement Neighborhoods 

(SCEN) program. The contrasts between SCEN and non-SCEN block groups were most notable in weaker middle markets, 

compared to areas identified as stable or strong middle markets. Neighborhoods participating in Minneapolis’ All Together Now 

(ATN) program for three or more years experienced a larger increase in sales price and greater declines in nuisance violations than 

neighborhoods that were not in the program or that were in the program for just one or two years; dosage is an important 

consideration (e.g. the level and longevity of treatment within a given neighborhood). Although the analysis of each program was 

not robust enough to determine a causal relationship, statistical significance, or generalizability, these findings suggest that 

sustained geographic targeting of code enforcement over multiple years has been effective in Baltimore and Minneapolis 

neighborhoods that fall between each city’s most stressed and more stable communities—where there is room for property 

condition improvement but not severe challenges with disinvestment. 

Demolition

 Dangerous Building Initiative, Kansas City, MO – Three-year program to demolish backlog of dangerous buildings, 

primarily single-family homes.

 City County Demolition Project, Minneapolis, MN – Three-year program to demolish vacant and dilapidated properties 

with multiple code violations.

Summary of Findings: The analyses indicated a positive association between demolition activity and housing market 

stabilization. The programs studied were not designed to stabilize specific geographies, but due to the spatial patterns of 

dangerous buildings, there were some concentrations of demolition activity, especially in Kansas City. In Kansas City, treated 

areas experienced much larger increases in homeownership and declines in housing vacancy than comparable areas without 

concentrated demolitions. The association between demolition and market stability was more limited in Minneapolis where 

vacancy decline was the only measure by which treated neighborhood substantially outperformed comparable areas. This quick, 

basic policy analysis does not show that targeted demolition will stabilize all neighborhoods, but in these two cases, we observed

a greater level of stabilization over time in treatment areas than in comparable areas, especially where demolition was spatially 

concentrated. The analysis does not establish causality, statistical significance, or generalizability.

Scattered Site Rehabilitation

 WPRE/Neighborhood Restorations Scattered Site Program, Philadelphia, PA – Private sector developers acquired 

and rehabilitated abandoned rowhouses into affordable single-family rental homes in West Philadelphia. 

 Atlanta Neighborhood Development Partnership Scattered Site Program (ANDP), DeKalb County, GA – Nonprofit 

acquired and rehabilitated foreclosed single-family homes for homeownership.

Summary of Findings: Neighborhood level associations between market stabilization and scattered site activity were mixed in 

these two non-governmental programs and there are inherent limits on geographic concentrations in a scattered site model. 

Areas with relative concentrations of West Philadelphia scattered site renovation activity had greater increases in sales prices 

than comparison areas, but on other measures, treated neighborhoods and comparable areas saw similar trends. In Dekalb 

County, GA, at a time of widespread sales price and property assessment declines, the decline in home values was slightly smaller 

1

2

3
© 2021 Fannie Mae Page 8 of 60

in areas around ANDP’s renovated properties and homeownership declines were also slightly less severe. A higher program 

dosage was associated with more notable change; treated areas with the most ANDP renovations were more likely to outperform 

their comparison areas than areas with the fewest ANDP renovations. The limits of the research design do not allow us to extend 

these findings to other types of markets or different versions of a scattered site model, nor can we establish causality or statistical 

significance.

Land Bank

 Houston Land Bank, Houston, TX – Quasi-public local government corporation acquires and transfers tax sale 

properties without structures to new owners for new construction that meets community needs such as quality 

affordable housing.

 Albany County Land Bank, Albany, NY – Nonprofit acquires tax foreclosed and other vacant or abandoned properties 

and sells them to homebuyers and developers.

Summary of Findings: There was an association between land bank activity and neighborhood stabilization in both programs. In 

Houston, land bank activity was concentrated in three market types—all on the weaker end of the city’s markets, and all with low 

median incomes. The most notable stabilization occurred in the strongest of the three markets. These markets also had the 

greatest concentration of land bank activity. In Albany, land bank activity was exclusive to the city’s most stressed low-income 

markets, making the identification of a non-treated comparison areas difficult. Differences did emerge in looking at areas with 

one or no land bank transactions and those with two or more; there was a larger increase in sale prices and decreasing vacancy in 

treated areas with more transactions. Similar to the code enforcement interventions, market context and program dosage may 

have played a role in these program locations. However, this analysis presents the same limitations regarding causality, statistical 

significance, and generalizability.

First Look Program 

 New Jersey Community Capital, Essex County, NJ – Nonprofit community lender obtained and renovated REO 

properties for first time homebuyers.

 Community Investment Corporation Community Initiatives Acquisition Program, Cook County, IL – Nonprofit 

community lender acquired distressed 1-4 unit housing and sold these properties to vetted private-market developers 

for homeownership or rental.

Summary of Findings: Analysis found observable market stabilization trends in areas within 500 feet of First Look properties in 

Essex County, NJ, but less clear results in Cook County, IL. Proximity to a First Look home in Essex County was associated with a 

larger decline in bank sales, a larger increase in median sales price, and an increase in sales volume. Proximity to a First Look 

home in Cook County was associated with an increase in median sales price and smaller decrease in sales volume relative to the 

comparison area but also with an increase in foreclosure. It is important to note that while these analyses focus on market 

changes within a small area, First Look programs are designed to treat properties rather than targeted geographies. As noted in 

the analyses of emergency bridge loans that follow, previous research has demonstrated impacts of individual foreclosed 

properties; this quick basic policy analysis did not capture all of the potential effects of the program. Causality, statistical 

significance, and generalizability could not be determined.

Emergency Bridge Loan/Soft Second Mortgage Program 

 Pennsylvania Homeowners’ Emergency Mortgage Assistance Program (HEMAP), Allegheny County, PA – Statewide 

program offers delinquent homeowners the right to apply for a short-term loan to make mortgage payments and while 

the application is pending foreclosure action cannot move forward.

 Delaware Emergency Mortgage Assistance Program (DEMAP), Wilmington, DE – Statewide program provides 

emergency bridge loans to homeowners who are at least 90 days delinquent on their mortgage loans due to 

circumstances beyond their control.

4

5

6
© 2021 Fannie Mae Page 9 of 60

Summary of Findings: These two statewide programs were designed to assist homeowners in need, wherever they live, and 

previous studies have shown emergency bridge loans benefit individual homeowners, near neighbors, and local government. 

However, there were not notable geographic concentrations of program activity, even in more densely populated areas of 

Pennsylvania and Delaware that had concentrations of foreclosure. Areas surrounding HEMAP recipients in Allegheny County, PA,

showed some signs of strength relative to areas further from recipients—larger increase in sales volume relative to the 

comparison areas but similar declines in foreclosures and median home sales price.

Statistical significance was not calculated due to the small number of treated properties and comparison points. Areas adjacent 

to DEMAP recipients did not show greater relative stability or improvement. The lack of association with neighborhood level 

stabilization may be related to market differences, lower density, program design (HEMAP includes an automatic “pause” on 

foreclosure proceedings that DEMAP does not) or other factors. To achieve an observable neighborhood stabilization effect, these 

programs would likely need to be more geographically concentrated. But it is important to note that while there is no consistently 

discernible market impact, these programs are not without impact entirely. To the contrary: every home that is saved from 

foreclosure has an estimated financial impact of more than $90,000. In a previous study, Reinvestment Fund estimated that the 

aggregate impact of foreclosures saved by Pennsylvania’s HEMAP program between 2013 and 2018 was just under $300,000,000.2

2

See: https://www.reinvestment.com/wp-content/uploads/2019/05/Reinvestment-Fund-Policy-Brief-HEMAP.pdf
© 2021 Fannie Mae Page 10 of 60

Introduction

Faced with the destabilizing impact of a global economic crisis such as the recent COVID-19 economic shock, local governments 

and their partners often turn to a standard set of policy interventions to stabilize low-income and middle neighborhoods. While 

governments and philanthropy spend billions of dollars each year on tools such as code enforcement, vacant property 

remediation, and foreclosure mitigation, practitioners often have very little information about what actually works. In Fall 2020, 

Fannie Mae partnered with May 8 Consulting, Inc., and Reinvestment Fund (Fannie Mae team) to evaluate six such interventions 

or tools to determine whether they had an impact on urban and suburban residential neighborhood stability based upon an 

analysis of key metrics such as vacancy, foreclosure rates and housing sale price. 

In order to provide information in a timely manner to partners on the ground working to address the adverse impacts of the 

COVID-19 economic crisis, the Fannie Mae team adopted a 6-month, 5-step basic policy analysis process.

Step 1

The Fannie Mae team began by performing a broad literature review of existing research and interviewing policymakers, 

academics, and program staff to identify the universe of common policy interventions or strategies being deployed to stabilize or 

improve either low-income areas (where median incomes ranged from 30%-60% of the local metro area median) or middle 

neighborhoods (where median incomes ranged from 70% to 130% of the local metro area median). This exploration identified 12 

interventions where there was some evidence of impact or where experts possessed an impressionistic assessment of success in 

stabilizing low-income and middle neighborhoods. They were:

Step 5 Analyze the relative effectiveness of the strategies on the 

stabilization of low-income and middle neighborhoods 

Step 4 Create a short case study defining basic program design and 

implementation

Step 3 Perform a basic policy analysis on the twelve programs

Step 2 Find two programs that used each strategy to stabilize one or more 

neighborhoods that are willing to share local data

Step 1 Identify six commonly used neighborhood stabilization strategies 
© 2021 Fannie Mae Page 11 of 60

Table 3: Eleven Policy Interventions with Some Evidence of Stabilizing Impact 

Step 2

The Fannie Mae team identified promising local programs that employed each strategy and reached out to program staff to 

determine whether they had collected data on the use of that strategy and were willing to share it for this analysis. The team 

contacted dozens of programs to identify two programs per category that could be analyzed. During this process of identifying

and reaching out to localities with programs, the team discovered that many programs were not implemented for the purpose of 

impacting one or more neighborhoods, or types of neighborhoods, but rather offered on a first-come first-serve basis to any 

resident of a state, county, or municipality. This lack of targeting constrained the program’s ability to measurably impact 

conditions within a neighborhood. In other cases, the intervention was part of a multipronged effort to intensively assist a specific 

neighborhood’s people and places with a large number of interconnected programs and services, rendering it impossible to 

assess the causal effects of any one intervention. Other programs did not maintain or were unable to share sufficient data 

documenting their efforts. Over time, the team was able to obtain data for two programs that employed one of the six selected

interventions among a diverse set of low-income and middle residential neighborhood types. The six interventions and twelve 

programs selected for analysis are:

Code Enforcement

Demolition

Land Banks

First Look Programs 

Scattered Site Development 

Emergency Bridge Loans/Soft Second Mortgages

Foreclosure Mitigation Counseling

Homeowner Repair Grants and Loans

Community Land Trusts

Access to Credit/Homeowner Downpayment Assistance

Greening of Vacant Lots/Sideyard Purchase Program
© 2021 Fannie Mae Page 12 of 60

Table 4: Selected Program Geographies

Step 3

The Fannie Mae team next collected and analyzed available data to determine whether and how each program impacted a set of 

neighborhood metrics. The team employed spatial adaptations of what are known as quasi-experimental designs within a 

framework of quick basic policy analysis. This framework emphasizes the use of the best data available within real world 

constraints in order guide policymakers in making swift decisions in contrast to purer academic research. In general, the research 

methods involved creating a reasonable set of matched areas (typically census tracts or block groups) wherein the intervention 

was applied in reasonable quantity to areas that were otherwise similar except for the presence of the intervention strategy. The 

team applied one of three types of comparison methods to each intervention: 1) using neighborhood market classifications from 

Reinvestment Fund’s Market Value Analysis where available; 2) identifying areas similar in terms of income, demographics, and

other factors; or 3) where comparable untreated areas were not feasible or when the treatment was not highly concentrated, 

comparing indicators within 500 feet of a treated property to the remainder of the census tract in which the property was located. 

Market indicators such as sales price, vacancy, and foreclosure were then analyzed in the treatment and comparison areas.

Step 4

The team also conducted a qualitative analysis of each program. Through interviews with staff and city leaders, the team gained a 

better understanding of how each program worked, how it was funded, its goals, and its strengths and shortcomings. The team 

then drafted short case studies summarizing the program and shared these summaries with program staff to ensure their 

accuracy.

Step 5 

Finally, the team reported out the findings for whether each strategy has a potential stabilizing impact on low-income or middle 

neighborhoods. 

This research aims to fill some critical gaps in knowledge regarding which neighborhood recovery strategies have the potential to 

stabilize or improve low-income and middle residential neighborhoods.

Code Enforcement

•Baltimore, MD

•Hennepin County, MN

Demolition

• Kansas City, MO

•Hennepin County, MN

Scattered Site Development

• Philadelphia, PA

•Dekalb County, GA

Land Bank

•Houston, TX

• Albany, NY

First Look Program 

• Essex County, NJ

• Cook County, Il

Emergency Bridge Loan/Soft Second Mortgage Program

• Allegheny County, PA

•Wilmington, DE
© 2021 Fannie Mae Page 13 of 60

Methodology 

The Fannie Mae team applied a quick basic policy analysis framework to this evaluation of six neighborhood market stabilization 

interventions. Based on the context for each intervention, the team selected one of three distinct methodologies within this 

overall framework, which are described below.

Quick Basic Policy Analysis

The original research conducted for this project applies an approach to policy analysis and evaluation that is uniquely 

appropriate in a moment when policymakers and practitioners desire to make data-based decisions that have a reasonable 

prospect to constructively address the ongoing impact of the COVID-19 pandemic. That approach is best described in the text by 

Patton, et al. (2013), as quick basic policy analysis.3 In this text, the authors juxtapose quick basic policy analysis to more 

traditional academic research designs. Quick basic policy analysis is needed when policy analysts are called on “to give advice to 

policy makers in incredibly short periods of time, in contrast to university researchers and think tank consultants who are hired 

specifically to conduct intensive research on public policy issues.” 

The circumstances around the implementation of an intervention may also support the use of a quick basic policy analysis 

framework; interventions intended to mitigate a crisis are unlikely to be applied under controlled experimental conditions. 

Policymakers and practitioners planning for a post-pandemic future are naturally looking back at similarly challenged periods 

such as the last foreclosure crisis for ideas about interventions that worked in hopes of identifying contemporary adaptations. 

Quite understandably, during a period when more than two million homes per year were going into a foreclosure status and more

than 800,000 jobs vanished, action was prioritized over controlled experimentation that would lend itself to academic evaluation. 

Indeed, the Fannie Mae team’s literature review confirmed that although there are many neighborhood stabilization intervention 

strategies, there is not a substantial amount of research demonstrating the overall efficacy of many of the strategies, and still less 

evidence of the unique situations under which a strategy may be more or less impactful. 

In doing this sort of post-hoc assessment of impact, when interventions were not implemented with an eye to evaluation, there 

are a number of challenges that impair the ability to draw definitive conclusions. The analysis does not establish causality, 

statistical significance, or generalizability. Yet, this quick basic policy analysis approach can yield useful insights.

Selection of Intervention Strategies for Study

The Fannie Mae team selected a set of 12 intervention strategies used in communities across the country since the foreclosure

crisis. The selection of strategies for review was driven by an interest in those which were intended or expected to stabilize a 

market, not bring it back from deep distress.4 But it was also, of necessity, guided by practical considerations. That is, the strategy 

selected needed to have been implemented in a quantity that could be impactful at a market level; one instance of housing 

rehabilitation in a neighborhood, although important for the beneficiary of the rehabilitated home, would not likely have a 

market affect and could not be detected with standard administrative or third-party market data. The implementation also 

needed to be recorded with sufficient diligence and detail (e.g., location, date, nature of intervention) such that impact could be 

measured. Lastly, the organization that did the implementation needed to be willing and able to share their data. These 

considerations reduced the number of candidate interventions significantly. Making this a greater challenge, the team was 

looking to select two implementations of each strategy in different places (e.g., targeted code enforcement in Baltimore and 

Minneapolis). 

Further methodological challenges include the potential that the programs selected for evaluation are different than those of the 

same strategy type not selected and thus the learnings from these may not generalize to all applications of the strategy. For 

example, a demolition program applied in a different type of market or with a substantially different level of demolition activity 

3

Patton, Carl V., David S. Sawicki and Jennifer J. Clark. 2013. Basic Methods of Policy Analysis and Planning, 3rd ed. Boston: 

Pearson.

4

Or if not to stabilize a market, to stabilize a homeowner/property which for evaluation purposes were clustered in places (e.g., 

neighborhoods). 
© 2021 Fannie Mae Page 14 of 60

would not be expected to yield similar results. There is also selection bias in that the places that implemented these programs 

may be different than similar places that did not implement these programs—such as local government capacity that may also 

lead to better outcomes for these neighborhoods. Another challenge is that there may well have been other stabilization activities 

going on in the place where the program was implemented. This confounds the ability to know where an observed market change 

was the result of the intervention of interest, the other things going on, or both. Stated differently, if targeted code enforcement 

and housing rehabilitation were going on in the same places, it is difficult to know with complete certainty whether any changes 

observed were a function of the code enforcement, housing rehabilitation, or both. The selection issue, taken together with the 

fact that the implementations were not carefully constructed from a controlled research perspective, present challenges to the 

ability to conclude with a high degree of certainty that a particular intervention caused a market to stabilize. But it is important to 

note that the validity of a controlled research-based conclusion tends not to be a Yes or No determination; there are degrees of 

certainty in conclusions about validity that quick basic policy analysis endeavors to push in the direction of more certain than 

uncertain. 

Analysis Strategies

To do this evaluation, the team adopted quick basic spatial adaptations of what are known as quasi-experimental designs—

“experiments that have treatments, outcome measures, and experimental units, but do not use random assignment to create the 

comparison from which treatment-caused change is inferred” (p. 6).5 These are a specific variety of quasi-experimental designs in 

that the control groups constructed are referred to as nonequivalent. As such, there are a number of limits to the validity of any 

conclusion about whether these analyses show the intervention was impactful or not; it is important to control as much as 

possible through the selection of comparable areas, and to clearly articulate the limitations of the conclusions. In general, the 

research methods involved creating a reasonable set of matched areas (typically census tracts or block groups) wherein the 

intervention was implemented in sufficient quantity to areas that were otherwise similar except for the presence of the 

intervention strategy.6 Small sample sizes also presented a challenge; in some cases the team added together more years or data, 

and in the cases where that was not a viable solution the team looked for an impact within a certain distance of each single 

intervention activity.

Because the overarching question was whether an intervention stabilized markets, home price change was used as an indicator 

of market stability in each analysis. Secondary indicators included, based on their relevance and availability, vacancy, 

foreclosure, code violations, and sales volume. In some instances, these were the articulated impacts of the interventions, while 

in others it could be inferred (e.g., citing properties that were believed to be exerting a blighting influence on surrounding 

properties). 

5

Cook, Thomas D., and Donald T. Campbell. 1979. Quasi-Experimentation: Design and analysis issues for field settings. Chicago: 

Rand McNally College Publishing Company. 

6

This raises another set of “selection” problems for controlled research purposes. How and why did the practitioners pick where 

they intervened? Was there some critically important element (e.g., the availability of a property to treat, affordable price points) 

that did not exist in comparison areas?
© 2021 Fannie Mae Page 15 of 60

The team had three general strategies for doing the comparisons: 

 Strategy 1: Compare all areas with treatments to non-treated 

areas in the same Market Value Analysis7 (MVA) category to 

see whether, as a group, treated areas outperformed 

comparable areas on stability indicators and, if multiple 

waves of the MVA were available, whether the treated areas 

improved sufficiently so as to rise to a stronger MVA category; 

 Strategy 2: Compare each area where an intervention was 

made to three comparable areas defined by characteristics 

such as sales price or household income vis a vis one or 

another performance measurement—typically a 

measurement that would be included in the creation of an 

MVA as a reliable indicator of relative market strength such as 

sale price, vacancy, or code violations; and 

 Strategy 3: Compare a 500-foot buffered area around a 

treated property to areas of the same census tract outside of 

that 500-foot buffer. 

In the end, the analysis strategies are based on an adaptation of a 

reasonably standard quasi-experimental design. The results that these 

analyses produced should be viewed through the lens of attempting to 

learn as much as possible from an experiment that was not organized 

or executed as a traditional research experiment. The analyses have 

shortcomings and, in the parlance of Cook, et al. (1979) “threats to the 

validity” of conclusions. But it is better to extract as much valuable 

information from these reasonably controlled, post-hoc evaluations 

for practitioners who are being called into action to address 

destabilized markets in the communities within which they work. 

7

Information about Reinvestment Fund’s MVA can be found at: https://www.reinvestment.com/policy-solutions/market-value-analysis/. 

See also: https://americanassembly.org/s/on-the-edge-final-pdf-2.pdf

About the Market Value 

Analysis Strategy

For six of the 12 intervention geographies, the team 

was able to draw upon Reinvestment Fund’s Market 

Value Analysis (MVA) as a resource: Houston, 

Baltimore, Philadelphia, Wilmington, DE, Allegheny 

County, PA, and Kansas City, MO. The MVA is a databased assessment of a community’s real estate 

market that Reinvestment Fund created and first used 

in Philadelphia in 2001; since then, the MVA has been 

replicated in a range of cities and regions, some of 

which have had MVAs completed every three years. It 

is based on a set of market indicators, generally 

reflected in address-level administrative data points 

such as: home sale prices, building permits, 

demolitions, subsidized rental housing and mortgage 

foreclosures. These indicators are summarized to the 

census block group geography and then analyzed 

using a statistical cluster analysis, the purpose of 

which is to identify categories of block groups that 

share a common set of conditions. Within any 

category, the block groups are very similar but the 

groupings themselves are different from each other. 

Typically, MVAs are field validated and subject to the 

input and scrutiny of a local group of subject matter 

experts.

By using the MVA in communities where one had been 

completed, the team was able to identify different 

areas in a city with very similar market conditions. 

Thus, if an intervention was clustered in several block 

groups within one MVA category, all else equal, 

comparing it to other block groups of the same 

category for which there was no “treatment” 

provided comparison between reasonably similar 

places. Matching of areas is done to effectively control 

extraneous factors. The MVA ensures matched places 

are as similar as possible. In instances where MVAs 

were completed both prior to the intervention and 

post intervention the team could see whether an area 

changed market categories. In that instance, the MVA 

itself became a critical performance measurement. 

MVAs are completed roughly every 3 years in localities 

where the analysis is updated.
© 2021 Fannie Mae Page 16 of 60

Research Findings: Basic Policy Analysis of Six Policy Interventions

Code Enforcement

Intervention Overview: Local governments have the authority to make and enforce laws to preserve public health and safety 

including the regulation of private property condition standards. Local governments historically provided reactive code 

enforcement, sending out an inspector in direct response to a complaint by a tenant or neighbor. Today, governments are 

proactively checking on the condition of residential properties to ensure they are in safe, healthy condition and that exterior 

conditions do not create a nuisance for surrounding properties. 

Prior Studies Regarding Neighborhood Stabilization Impact: Studies show that well designed proactive code enforcement 

initiatives with consistent inspections can improve the condition of the municipality’s housing stock and reduce the number of 

unsafe or substandard rental properties. Some evidence supports the ability of minimum vacant building condition standards to 

trigger new investment, raise surrounding values, and reduce crime. 

Summary of Findings: These two programs were the most geographically targeted of all the programs evaluated and showed the 

strongest association between program activity and neighborhood stabilization. There was observable improvement and 

relatively more stability in middle markets that were treated by Baltimore’s Streamlined Code Enforcement Neighborhoods 

(SCEN) program. The contrasts between SCEN and non-SCEN block groups were most notable in weaker middle markets, 

compared to areas identified as stable or strong middle markets. Neighborhoods participating in Minneapolis’ All Together Now 

(ATN) program for three or more years experienced a larger increase in sales price and slightly greater declines in nuisance 

violations than neighborhoods that were not in the program or that were in the program for just one or two years; dosage is an 

important consideration (e.g. the level and longevity of treatment within a given neighborhood). Although the analysis of each 

program was not robust enough to determine a causal relationship, these findings suggest that sustained geographic targeting of 

code enforcement over multiple years has been effective in 

Baltimore and Minneapolis neighborhoods that fall between each 

city’s most stressed and more stable communities—where there is 

room for property condition improvement but not severe 

challenges with disinvestment. 

Note on Quick Basic Policy Analysis: The analyses presented 

here on these two programs do not examine causal links or 

statistical significance between the interventions and 

neighborhood stability and cannot be generalized to all local 

instances of an intervention type. They document market changes 

related to neighborhood stability in areas that were “treated” by a 

certain intervention.

Program 1: Baltimore Streamlined Code Enforcement Neighborhoods Program (SCEN)8

 Program description: In 2010, Baltimore launched its Streamlined Code Enforcement Neighborhoods program targeted 

at vacant buildings and lots in middle neighborhoods. The city designated 88 Streamlined Code Enforcement 

Neighborhoods (SCENs) as the focus for the program. In each of the neighborhoods, vacant properties were scattered 

rather than concentrated, and market conditions were believed to be strong enough so that proactive code enforcement 

was likely to either motivate owners of vacant properties to restore them to use or render the properties desirable 

candidates for sale and rehabilitation through the city’s receivership process. The first step in the Streamlined Code 

Enforcement process is for an inspector to issue a Vacant Building Notice that requires the owner to restore the property. 

The inspector then returns in 30 days to post the vacant building notice and confirm that the violations remain unabated. 

8

Information for the program description was obtained from three interviews with three code enforcement leaders within the 

Baltimore City Department of Housing and Community Development conducted on January 28, 2021: Deputy Commissioner Eric 

Booker, Assistant Commissioner Kathleen Byrne, and former Chief of Policy & Partnerships Robert Pipik.

1

There was observable 

improvement and relatively more 

stability in middle markets that 

were treated by Baltimore’s 

Streamlined Code Enforcement 

Neighborhoods (SCEN) program.
© 2021 Fannie Mae Page 17 of 60

The inspector will also attempt to contact the owner using the city’s registration database. If the owner fails to take 

action, a letter is sent explaining that the owner will be issued a $900 citation if the owner does not contact the city and 

demonstrate why additional enforcement actions are unnecessary. If the owner fails to contact the City or is unable to 

demonstrate an ability to abate the conditions cited in the Vacant Building Notice, a $900 citation is issued. During the 

next inspection, a second $900 citation is issued, and the case moves to an attorney at the city’s housing department for 

further action. When the owner fails to act to pay the citation and address code violations, the city determines whether 

to file a receivership action. In a receivership action, the city files an action with the District Court of Maryland to appoint 

a receiver to sell the property at auction to an eligible high bidder with the capacity to reactivate the property. A local 

attorney, in 2004, formed a nonprofit called One House at a Time (OHAAT) whose primary function is to act as a court 

appointed agent (receiver) to convey vacant and nuisance properties to qualified buyers. The minimum bid at auction is 

$5,000, and the winning bidder commits to rehabilitating the property within one year. To be an eligible bidder, OHAAT 

requires bidders to prove they have the capacity and experience to renovate the property. OHAAT then administers an 

auction at a judicial sale where they sell to the highest eligible bidder and monitor permit activity to ensure the new 

owner rehabilitates the property within the set timeframe. Receivership provides the new owner with a clean title free of 

all liens on the property. The goal of the program is compliance, and the city attempts to gain the owner’s attention and 

voluntary action at every step of the process. Most new owners choose to rent the property as Baltimore rents are often 

sufficient to cover costs of rehabilitation while sales prices typically are not adequate.

 Relevant state or local law: Building, Fire and Related Code Sect. 121 

https://legislativereference.baltimorecity.gov/sites/default/files/Art%2000%20-%20Bldg,%20Fire_0.pdf; ICC Code 2018

 Duration: 2010 to present 

 Goal: To maintain safe and attractive neighborhoods throughout the city

 Agency that administers: Baltimore City’s Department of Housing and Community Development

 Budget and staffing: Annual budget of approximately $9 million. Have approximately 80 inspectors performing property 

maintenance inspections, as well as office support staff.

 Key partners: OHAAT, District Court of Maryland, Department of Public Works (cleans up vacant properties and boards 

buildings), 311 (takes resident complaints), and the Baltimore Police (responds to complaints that may prove dangerous 

for inspectors). 

 Funding: City provides the $9 million budget through general budget allocations. 

 Key Takeaways

 Good parcel level data is critical to strategic code enforcement.

 Different markets require very different code enforcement approaches, so a good neighborhood market study is 

invaluable in targeting efforts.

 Need a competent receiver partner to convey vacant and nuisance properties to qualified buyers.

 It takes many years to make a difference in a neighborhood so need a stable program that will not shift with each 

new Mayor.

Data Analysis 

Overview and Methods: The goal of the analysis is to examine if the Streamlined Code Enforcement Neighborhoods (SCEN) 

program impacted vacancy rates and sale prices in census block groups treated with the SCEN program. The timeframe for 

analysis is from 2010, when receivership actions first were targeted to SCEN areas, to 2020 for administrative data indicators; 2019 

ACS figures were the most recent available for population and tenure. The geography for the analysis is census block groups 

(SCEN are collections of block groups). Sales and vacancy data were provided by the Baltimore Department of Housing and 

Community Development. Race, ethnicity, housing unit, and population data were obtained from the American Community 

Survey 5-year estimates.

Census block groups in SCEN were compared to similar block groups that were not in SCEN on net change in number of vacant 

properties, vacant properties as a share of residential parcels, and change in median sale price from 2009/2010 to 2018/2020. 
© 2021 Fannie Mae Page 18 of 60

Block groups were treated as similar if they were in the same category in Reinvestment Fund’s 2011 Market Value Analysis (MVA), 

which identified ten distinct market categories ranging from strong (A) to distressed (J). Three “middle market” categories were 

examined—Middle Market Choice, Middle Market, and Middle Market Stressed (categories C, D, and E, respectively, in the 2011 

MVA). 

Table 5: Characteristics of Treated and Comparable Areas, Streamlined Code Enforcement

Middle Market Stressed Middle Market Middle Market Choice

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

# Block Groups 80 29 78 11 84 29

Median Household Inc. $46,700 $42,600 $52,900 $41,000 $70,800 $67,600

Median Sales Price, 09/10 $70,800 $68,200 $106,500 $102,200 $155,500 $153,000

Housing Units / Sq. Mi. 3,800 5,870 2,230 5,280 3,240 3,960

% Black 77.40% 52.30% 71.80% 85.20% 57.20% 61.00%

% White 13.50% 31.20% 17.60% 8.70% 34.60% 29.80%

% Hispanic 5.40% 9.80% 6.60% 3.60% 4.20% 3.00%

% Asian 1.40% 1.80% 1.10% 0.50% 1.60% 2.50%

Findings: There were signs of stabilization in middle markets that were treated by Baltimore’s Streamlined Code Enforcement 

Neighborhoods (SCEN) program. The potential impact of SCEN, when contrasted with comparable block groups, was most 

notable in weaker middle markets. Vacancy (as a share of residential properties) rose less quickly in SCEN block groups than in 

similar block groups that were not treated by the SCEN program. Median sale prices rose more in SCEN block groups that were 

Middle Market Stressed (13.6%) and Middle Market (23.3%) than in similar block groups that were not treated by the SCEN 

program (0% and 20.6%, respectively). In Middle Market Choice SCEN block groups, sale prices rose (14.2%) but not as much as in 

Middle Market Choice block groups that were not treated by the SCEN program (15.7%). The population declined less in SCEN 

Stressed Middle Markets.

Table 6: Changes in Treated and Comparable Areas, Streamlined Code Enforcement 

Middle Market Stressed Middle Market Middle Market Choice

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

# Block Groups 80 29 78 11 84 29

First Quartile* Net Change in # Vacant 

Properties, 2010-2021 0 -3 0 0 0 0

Median Net Change in # Vacant 

Properties, 2010-2021 4 6 1 1 1 0

Third Quartile Net Change in # Vacant 

Properties, 2010-2021 8 17 4 4 3 1

% Vacant Properties (of Residential 

Prop.), 2010 2.0% 5.2% 0.3% 0.3% 0.3% 0.0%

% Vacant Properties (of Residential 

Prop.), 2021 3.6% 7.1% 1.1% 1.5% 0.6% 0.5%
© 2021 Fannie Mae Page 19 of 60

Sale Price Change, 2009/2010-

2018/2020 14% 0% 23% 21% 14% 16%

Population, 2010 73,575 29,441 87,268 9,843 85,553 30,205

Population, 2015/2019 71,165 26,983 90,460 10,874 84,560 30,593

Avg. Change 2010 - 2015/2019 -4.8% -6.3% 3.1% 7.7% -1.4% 11.3%

% Owner Occupied, 2015-2019 52.7% 38.7% 58.5% 52.0% 57.9% 70.0%

*Quartiles of block groups by change in the net number of vacant properties; the first quartile of treated block groups in Middle Market Choice 

had no change in the number of vacant properties or a reduction in the number of vacant properties

Limitations: A limitation of the analysis is that there are clear spatial patterns to the change in vacancy. A map of the net change 

in vacant properties from 2010 to 2020 is below. Vacancy appears to spread from block group to block group. A more 

sophisticated research design could take this into account, which could credit the SCEN program with preventing increases in 

vacancy that might otherwise be expected based on the proximity of SCEN-treated block groups to high-vacancy areas.

Map 1: Vacancy Change in Treated and Comparable Areas, Baltimore Streamlined Code Enforcement
© 2021 Fannie Mae Page 20 of 60

Program 2: Minneapolis All Together Now Program (ATN)9

 Program description: In 2013, Minneapolis launched the All Together Now (ATN) program to proactively engage 

residents and associations in neighborhoods prior to exterior code inspections. ATN is part of a larger organizational shift 

from enforcement to engagement. Under the program, neighborhood associations, property owners, and residents 

receive information and education about what violations inspectors will be looking for and when inspectors will be 

coming to their neighborhood in advance of a coordinated inspection. ATN neighborhoods are selected based upon data 

evaluating levels of nuisance code violations. Typically, neighborhoods that have been in ATN have around twice as 

many nuisance violations as non-ATN neighborhood. ATN operates between June and September in 10-13 

neighborhoods per year. The program was at first limited to just a few neighborhoods but expanded over time. Outreach 

includes a friendly photo-filled, easy-to-read flyer specifying the violations that inspectors will be looking for including 

high grass, trash, and junk cars, along with discount coupons for use at local hardware stores on property maintenance 

related tools and supplies. Inspections are performed by the district inspector, typically from his or her car, during 

business hours. Some neighborhoods have asked for the exterior inspections to occur in tandem with their clean-up day 

or other neighborhood event and the city makes every effort to do so. In the first two years of the program, the city 

performed significant government-funded abatement of the worst public nuisance conditions around vacant properties 

and sought reimbursement through tax liens. 

 Relevant state or local law: No additional legal authority needed.

 Duration: 2013 - present

 Goals: To engage neighborhood associations, businesses, and residents to help make neighborhoods a safe and clean 

place to live and increase voluntary code compliance

 Agency that administers: Department of Regulatory Services

 Budget and staffing: Inspections in ATN neighborhoods in 2019 cost approximately $80,000 (2,2286 hours at $35.00 per 

hour). The program also required a community engagement specialist to work with all residents but more intensively in 

ATN neighborhoods who cost an additional $12,000. Marketing and communication to residents cost about $2,600.

 Key partners: Neighborhood associations help get the word out and local businesses provide a discount to owners on 

tools and supplies related to property maintenance.

 Funding: Department of Regulatory Services operates ATN without additional funding or personnel

 Key Takeaways

 Neighborhoods want differing levels of engagement and have different requests for the program to be effective. For 

example, some neighborhoods want data on property condition while others want a trash truck for a day to handle 

dumped trash.

 City’s initial determination to select ATN neighborhoods solely based upon data of public nuisance violations 

resulted in repeated returns to the same distressed neighborhoods with high concentrations of persons of color, so 

the city started looking at percent change in nuisance violations and branched out into other neighborhoods to 

ensure equitable enforcement.

 Notifying tenants, in addition to property owners, may be important if tenants are responsible for keeping the yard 

or property exterior maintained.

9

Information for the program description was obtained from three interviews with three code enforcement leaders within the 

Minneapolis Department of Regulatory Services conducted on February 18, 2021: Kellie Jones, Director Housing Inspection 

Services, Caitlin Maxwell-Glenn, Community Relations Specialist and Kira Hasbargen, Grants and Community Engagement 

Manager.
© 2021 Fannie Mae Page 21 of 60

Data Analysis 

Overview and Methods:

The goal of this analysis is to understand how targeted 

nuisance code enforcement under the All Together Now 

(ATN) program impacted property sales prices and the 

number of nuisance code violations in treated 

neighborhoods. Nuisance violations are rubbish, tree, grass, 

and inoperable vehicle violations. The code enforcement 

activity examined in this analysis occurred between 2014 

and 2019 (although the number of years in which 

neighborhoods participated in ATN varied). To understand 

how these activities impacted the local housing market, the 

team analyzed changes in home sales prices between 

2015/2016 (the earliest years for which data was available, 

which represent a mid-treatment period rather than a pretreatment baseline) and 2018/2019 and nuisance code 

violations between 2012/2013 and 2018/2019.

The sales price data from 2015-2019 was downloaded from 

Open Data Minneapolis, courtesy of the Minneapolis Assessor’s Office. Code enforcement nuisance violations from 2012 to 2019 

were provided by the City of Minneapolis – Regulatory Services. The City of Minneapolis’ Regulatory Services department shared 

which neighborhoods10 participated in ATN and the number of years those neighborhoods were part of the ATN program. 

Neighborhoods with median single family home sales prices under $250,000 in 2015/2016 (the highest median sales price for an 

ATN Neighborhood) and at least 100 residential parcels, but that did not participate in ATN, were selected as comparisons.

The analysis compares the changes in sales prices and number of properties with nuisance code violations within neighborhoods 

that participated in ATN for one or two years, 3 or more years, and similar neighborhoods which did not participate in ATN. The 

ATN Neighborhoods started with much higher numbers of nuisance violations and properties with two or more violations.11

Table 8: Changes in Treated and Comparable Areas, All Together Now

Neigh. 1-2 Years in ATN Neigh. 3+ Years in ATN Comparable Areas

# Neighborhoods 10 11 32

Median Sales Price, 2015/2016 $160,000 $127,000 $191,000

Median Sales Price, 2018/2019 $205,000 $170,000 $241,500

Median % Change in Sales Price 27% 36% 28%

Med. # Nuisance Violations, 2012/2013 634 1051 325

Med. # Nuisance Violations, 2018/2019 487 629 197

Change in # Nuisance Violations -28% -32% -42%

2+ Violation Properties, 2012/2013 199 386 79

10Minneapolis Open Data provided a spatial definition of Minneapolis neighborhoods and Reinvestment Fund confirmed that 

these definitions are consistent with those used both by Regulatory Services and the Assessor’s property sales data.

11Regulatory Services uses properties with two or more nuisance violations as a proxy for the worst condition properties, so the 

team examined both all properties with a nuisance violation and those properties with two or more nuisance violations.

Treated Areas Comparable Areas

# Neighborhoods 21 32

% Black 27% 17%

% White 46% 61%

% Hispanic 13% 12%

% Asian 7% 4%

Med. HH Income $86,900 $105,300

Housing Units per 

Square Mile 3,329 3,239

Table 7: Characteristics of Treated and Comparable Areas, 

All Together Now
© 2021 Fannie Mae Page 22 of 60

2+ Violation Properties, 2018/2019 135 121 39

Change in 2+ Violation Properties -37% -53% -49%

Median % Violations (per Res. Parcel), 

2012/2013 51% 84% 57%

Median % Violations (per Res. Parcel), 

2018/2019 45% 43% 32%

Change in % with Nuisance Violations -6% -41% -25%

% 2+ Violation Properties (per Res. 

Parcel), 2012/2013 17% 28% 13%

% 2+ Violations Properties (per Res. 

Parcel), 2018/2019 11% 11% 7%

Change in % with 2+ Violation Properties -6% -17% -6%

Findings: Neighborhoods in the ATN program for three or more years experienced a larger increase in sales price (as a 

percentage) than other neighborhoods that were not in the program or neighborhoods that were in the program for one or two 

years, although the size of prices increases was similar. The number of nuisance violations and properties with two or more 

violations greatly decreased in neighborhoods that were in the ATN program for three or more years, including compared to the 

other two groups of neighborhoods. The most notable distinction for 3+ year ATN neighborhoods was their much larger decline in 

the share of residential properties that had one or multiple violations. 

Limitations: Some limitations of the analysis are small sample size and that property sales data was only available for 2015-2019, 

while ATN activities began in 2014 in some neighborhoods. Code Enforcement nuisance violations decreased by a large amount 

throughout Minneapolis in the time period investigated, raising the question about whether the data represent improvement in 

property conditions or change in enforcement practices and whether the observed changes in ATN neighborhoods were caused 

by ATN activities or some other factor across the city generally.
© 2021 Fannie Mae Page 23 of 60

Demolition 

Intervention Overview: Demolition to eliminate individual dangerous or blighted buildings has become a commonly used tool 

by local governments.12 Individual residential structure demolitions are typically authorized under a local government’s power to 

abate a public nuisance after providing the owner due process protections. Prior to demolition, a municipality will typically use its 

code enforcement authority to cite the property for violations and inform the owner of a specific timeframe within which they 

must fix the violations or demolish the structure. Where the owner fails to comply, the local government can then abate the 

nuisance by demolishing the structure. The municipality then places a demolition lien on the property or the property’s tax bill in 

order to receive reimbursement from the owner for the expense. Federal funding programs that may be used for demolitions 

since the Great Recession include HUD Community Development Block Grants (CDBG), the Neighborhood Stabilization 

Program,

13 and Hardest Hit Funds.14

Prior Studies Regarding Neighborhood Stabilization Impact: Studies find that demolition of distressed properties increases 

surrounding home prices and reduce nearby abandonment. While the vacant lots created by demolition still depress housing 

values, their impact had a significantly reduced negative impact when compared to distressed structures. Studies show differing 

impact depending upon neighborhood type and offer conflicting findings as to whether demolition reduces either foreclosures of 

surrounding properties or crime.

Summary of Findings: The analyses indicated a positive 

association between demolition activity and housing market 

stabilization. The programs studied were not designed to stabilize 

specific geographies, but due to the spatial patterns of dangerous 

buildings, there were some concentrations of demolition activity, 

especially in Kansas City. In Kansas City, treated areas experienced 

much larger increases in homeownership and declines in housing 

vacancy than comparable areas without concentrated demolitions. 

The association between demolition and market stability was more 

limited in Minneapolis where vacancy decline was the only measure 

by which treated neighborhood substantially outperformed comparable areas. This quick, basic policy analysis does not show 

that targeted demolition will stabilize all neighborhoods, but in these two cases it was associated with stabilization, especially 

when spatially concentrated. 

Note on Quick Basic Policy Analysis: The analyses presented here on these two programs do not examine causal links or 

statistical significance between the interventions and neighborhood stability and cannot be generalized to all local instances of 

an intervention type. They document market changes related to neighborhood stability in areas that were “treated” by a certain 

intervention.

12Hackworth J. Demolition as urban policy in the American Rust Belt. Environment and Planning A: Economy and Space. 

2016;48(11):2201-2222. doi:10.1177/0308518X16654914 

13HUD's Neighborhood Stabilization Program funded 24,425 demolitions from 2009-2016. The Obama Administration’s Efforts To 

Stabilize the Housing Market and Help American Homeowners, U.S. Department of Housing and Urban Development Report 

(December 2016) https://www.hud.gov/sites/documents/SCORECARD_2016_12_508C.PDF

14U.S. Treasury obligated Housing Finance Agency Innovation Fund for Hardest Hit Markets (HHF) $9.6 billion in Trouble Asset 

Relief Program funds to 19 state housing finance agencies (HFAs). HFAs use funds to implement programs that address

foreclosure and help stabilize local housing markets—for example, by demolishing blighted properties. Congress extended HHF in 

2015, and HFAs must disburse all HHF funds by December 31, 2021, or return them to Treasury.

2

Studies find that demolition of 

distressed properties increases 

surrounding home prices and 

reduce nearby abandonment.
© 2021 Fannie Mae Page 24 of 60

Program 1: Kansas City Dangerous Buildings Initiative15 

 Program description: In 2016, the city launched a three-year, $10 million effort to demolish the backlog of buildings on 

its dangerous buildings list. The majority of the properties to be demolished were single-family houses that were 

identified by the Fire Department after a fire on the premises, often caused by unauthorized occupants. Prior to 

beginning demolitions, the city refined its definition of dangerous buildings to ensure that it would not demolish viable 

houses that could be repaired for less than the $8,000 to $11,000 it would cost to demolish them. 300+ single-family 

houses came off the city’s dangerous list as a result of this reevaluation, reducing the backlog to slightly over 800. The 

initiative began with the demolition of 200 public properties owned by the land bank. The Kansas City Land Bank offered 

to sell any of these properties for $1 to buyers with the capacity to rehabilitate the properties and even threw in a grant 

of $8,500 for rehabilitation, money that they would have spent on demolition. Approximately 45 properties were 

purchased for rehabilitation under the $1 program. The city also staffed up and began to provide legally required notice 

to anyone with an ownership or financial interest in the 600 privately owned properties on the dangerous properties list. 

Owners were told to repair or demolish the property within 60 days, or it would be slated for demolition. Owners were 

also asked to allow an internal inspection to confirm the property was unsafe. Where the owner refused to allow an 

interior inspection or was unresponsive, the city obtained a search warrant to evaluate the interior condition. 

Approximately 30-35% of owners of the 600 privately owned properties took sufficient action to remove the property 

from the demolition list. In total, 895 demolitions were carried out by six contractors over two years for a budget of $8 

million. Demolitions were contracted in packages of up to ten jobs in close proximity. Prior to demolition, the city hired a 

contractor to perform a hazardous materials evaluation and remediate the asbestos or other toxic materials where 

found. Liens for demolition and asbestos remediation were placed on the owner’s tax bill. The remaining $2 million of 

the $10 million budgeted was used to perform a reduced number of priority demolitions in 2019 and 2020.

 Relevant state or local law: Section 56-532 of the Kansas City Code of Ordinances

 Duration: 2016-2018

 Goals: Reduce blight by eliminating the backlog of 1200 dangerous properties on the demolition list. Be responsive to 

residents surveyed who stated that demolishing unsafe structures was a top priority

 Agency that administers: Neighborhoods and Housing Services Department Dangerous Buildings Section

 Budget: One-time $10 million allocation from Council became available May 2016. Average cost for the demolition of a 

single-family house was $11,000.

 Key partners: Land Bank readied about 200 publicly owned properties to be demolished and offered $1 home program. 

Water Department coordinated water shut off in each property. Industrial Salvage & Wrecking and Kissick Construction 

donated 65 free demolitions as a community service.

 Funding: One time bond issue of $10 million

 Key Takeaways

 Significant need to streamline process and reduce paperwork permitting demolitions

 Faced with a clear threat of demolition, approximately 1 in 3 owners will take action to rehabilitate, sell, or demolish 

the structure on their property

 Majority of lots will remain vacant after demolition of the structure particularly where the lot size is considered 

undevelopable under current zoning and building codes

 Resident satisfaction with city blight efforts did not improve even after 895 demolitions were performed. It is unclear 

why dissatisfaction persisted.

15Program description information obtained through interview with Kate Bender, Office of City Manager on January 19, 2021.
© 2021 Fannie Mae Page 25 of 60

Data Analysis

Overview and Methods: This analysis compared changes in census block groups which received concentrations of demolition 

activity from 2016 through 2018 and similar census block groups that did not. Changes in home prices, ownership, and vacancy 

were measured between 2014 and 2020. 

Treatment Areas were those that experienced over 10 demolitions between 2016 and 2018 and fewer than 10 demolitions in prior 

years (11 block groups). Most block groups were located in one of the city’s weakest housing markets, identified in the City’s 2016 

MVA16 as H or I, while one block group was located in an F market. The average treated area had 14 demolitions between 2016 and 

2018 and 4 demolitions between 2012 and 2015. In total, treated areas had a total of 153 demolitions between 2016 and 2018. 

For each treated block group, the team identified two comparable block groups that had under 10 demolitions between 2016 and 

2018 and were located in the same 2016 MVA category. To further refine the comparison areas, only block groups with similar 

median sales prices and vacancy rates in 2016 were selected. The average comparison area experienced 2 demolitions between 

2012 and 2015 and 4 demolitions between 2016 and 2018. Finding two comparable areas in the same MVA category and with 

similar home prices and vacancy rates was challenging, as many similar areas in distressed markets also experienced high 

numbers of demolitions. All of the areas analyzed below were located in an urban area.

This analysis uses data from the City of Kansas City for sales price of residential properties sold between 2014 and 2016Q2 & 2018 

and 2020Q2, and the percent of residential properties on the city’s vacant property registry, owned by a bank, with a vacant 

property violation, on the city's dangerous buildings list, or with a demolition permit issued in 2014-15 or 2019-20. Home 

ownership is drawn from the American Community Survey, Five Year Estimates, 2010-14 & 2015-19. All data is block group level. 

The table below describes the characteristics of treated and comparable areas in 2010-2014. 

Table 9: Characteristics of Treated and Comparable Areas, Dangerous Buildings Initiative

Treated Areas 

(Block Groups)

Comparable Areas 

(Block Groups)

# Block Groups 11 22

% Black 54% 68%

% White 12% 10%

% Hispanic 30% 15%

% Asian 1% 3%

Average Housing Units per Square Mile 2,166 2,538

Average Median Household Income $24,303 $23,953

Findings: The analysis indicates that areas with targeted demolition activity experienced housing market stabilization in 

subsequent years. Treated areas experienced much larger increases in homeownership and declines in housing vacancy than 

comparable areas without concentrated demolitions. However, though both areas saw increases in housing values, the average 

home price gains experienced in treated areas were not as large as those experienced in comparable areas without demolitions.

16The Market Value Analysis (MVA) is an analytic developed by Reinvestment Fund that uses statistical analysis of administrative 

data to define a range of up to 10 market categories within a geography, each assigned an ordinal letter with “A” being strongest. 
© 2021 Fannie Mae Page 26 of 60

Table 10: Changes in Treated and Comparable Areas, Dangerous Buildings Initiative

Neighborhoods Treated Comparable Areas

Median Sales Price, 2011 $12,328 $13,509

Median Sales Price, 2018 $29,682 $39,614

Change in Median Sales Price +$17,354 (+141%) +$26,104 (+193%)

% Homeowners, 2010-14 44.2% 48.5%

% Homeowners, 2015-19 54.5% 50.3%

Percentage Point Change in Ownership Rate +10.3% +1.8%

% Vacancy, 2015 29.1% 19.6%

% Vacancy, 2020 22.4% 17.3%

Change in Vacancy -6.7% -2.3%

The table below shows the number and proportion of comparable areas (i.e., block groups) that each treated area outperformed 

(each treated area had 2 comparable areas). Viewed this way, it is clear that performance across each of the treated areas was not 

consistent. While many of the treated areas outperformed both of their comparable areas in terms of vacancy and 

homeownership, only two saw larger home price increases than both of their comparable areas. These two areas were notable for 

having the highest number of demolitions over the treatment period. 

Table 11: Comparable Areas Outperformed by Treated Areas, Dangerous Buildings Initiative

Number of Comparable Areas that Treatment Areas 

Outperformed (0, 1, or 2 out of 2)

Block Group ID # of 

Each Treated Area

MVA 2016 # Demolitions in Treated 

Areas, 2016 – 2018

Increase in Home 

Prices

Decline in 

Vacancy

Increase in 

Ownership

290950161002 F 11 0 2 2

290950061003 H 12 1 0 1

290950165002 H 11 1 2 0

290950019004 I 17 2 2 2

290950021003 I 13 0 0 1

290950022003 I 14 1 1 2

290950034003 I 14 0 2 2

290950034004 I 12 1 1 2

290950160002 I 12 1 2 1

290950164001 I 14 0 2 2

290950164002 I 23 2 1 1

# of Comparable Areas Outperformed by All Treated Areas 9 15 16

% of Comparable Areas Outperformed by All Treated Areas 41% 68% 73%
© 2021 Fannie Mae Page 27 of 60

Limitations: The analysis was limited by the intervention’s recent implementation; the data available on sale prices and tenure 

changes may reflect periods too close to the intervention to fully capture change. Finding comparable neighborhoods without 

demolitions during this period was difficult and establishing statistical significance was not possible due to relatively small 

numbers of demolitions and home sales. 

Program 2: Minneapolis City County Demolition Project17

 Program description: In 2008, Minneapolis began the City County Demolition Project, a $1.7 million effort to demolish 

vacant and dilapidated properties. The City demolished 119 properties between 2008 and 2011 using federal 

Neighborhood Stabilization Program (NSP) dollars. The demolished properties were typically owned by an individual, an 

LLC, a corporation, or a financial institution. The properties were all vacant and usually had multiple code violations. The 

city selected the properties to be demolished from the city’s Vacant Building Registration list by identifying those that 

were too deteriorated to be economically feasible for rehabilitation and were subject to multiple resident complaints or 

visits by police and fire. Each owner or party with a potential interest in the property received an order to raze and 

remove the building. The identification of the owner, notice, and due process requirements took an average of two 

months to complete. The City also restructured its demolition bidding process away from a standard one property to 

“batched bids” where multiple nearby properties were packaged into one bid. The cost of demolition was assessed to 

the owner’s tax bill and where repaid, was recycled back into the demolition fund to finance further demolitions. The 

cost per demolition from 2008–2011 was approximately $15,000 with the presence of hazardous materials such as 

asbestos raising the cost. 

 Relevant state or local law: No change to law needed

 Duration: 2008–2011

 Goal: To eliminate blighted, vacant properties that were too deteriorated for rehabilitation to be economically feasible

 Agency that administers: Minneapolis Department of Regulatory Services

 Budget and staffing: 3-4 full time staff in 2008 at an approximate cost of $450,000 in staffing dropped to 2 full-time 

employees in 2010 and 2011. 

 Key partners: Received helpful input on the selection of properties to be demolished from community residents and the 

Fire and Police Departments

 Funding Sources: City received $5 million in Federal Neighborhood Stabilization Program (NSP) dollars of which $1.7 

million was allocated for demolition. Staff and operations were paid from the city budget. 

 Key Takeaways:

 Restructure and repurpose existing staff to respond to an immediate condition. Staff within the Problem Properties 

Unit was temporarily moved from existing duties around rental housing.

 Bid nearby demolitions together to reduce cost. 

 Create a structured, timely, and accountable process to select properties, provide due process to protect the 

owner’s rights, and contract with demolition experts. 

 Use threat of demolition to hold owners accountable and motivate action to avoid demolition. 

Data Analysis

Overview and Methods: To investigate the association between clustered demolition activity funded by NSP and changes in 

housing market conditions, the team examined areas where the City conducted the greatest concentration of demolitions 

occurring between 2009 and 2013 to similar census block groups that did not experience concentrations of demolitions. This 

17Information for this program description was obtained through an interview with Scott Bockes, Operations Analyst for 

Department of Regulatory Services on January 25, 2021.
© 2021 Fannie Mae Page 28 of 60

period varies from the core City County Demolition Project period of 2008-2011 because program data was not available and the 

team instead used data collected in Reinvestment Fund’s 2016 NSP-Neighborhood Investment Cluster Analysis, which included 

demolitions conducted through this and other programs, as well as metrics. The small number of demolitions each year also led

the team to expand the number of demolition years included. Changes in home prices, sales volume, and vacancy were measured 

between 2008 and 2014. Treated areas were clusters of contiguous block groups (NICs) where substantial numbers of NSP-funded 

activities had occurred. The average treated NIC area had 8.6 demolitions between 2009 and 2013, with a total of 69 demolitions 

occurring across the 8 NIC areas. For this analysis, treated areas were defined as NICs within the City of Minneapolis where more 

than 5 demolitions occurred between 2009 and 2013. Each NIC was matched with 3 comparison block groups, with similar 

characteristics (trends in home prices, foreclosure risk), but did not receive concentrated NSP investments. In order to select a 

sufficient number of comparison areas with similar market characteristics but fewer demolitions, the team has to include some 

block groups that were outside the city but still in Hennepin County.

The table below describes the racial composition, housing density, and average median household income in treated and 

comparable areas 2008-2012. The block groups analyzed in this analysis were located in a mix of urban and suburban areas in 

Hennepin County. 

Table 12: Characteristics of Treated and Comparable Areas, City County Demolition Project

Treated Areas Comparable Areas

# Block Groups 8 7

% Black 41% 22%

% White 22% 47%

% Hispanic 13% 18%

% Asian 15% 2%

Percent Other 9% 11%

Average Housing Units per Square Mile 2,801 2,977

Average Median Household Income $33,129 $33,957

The analysis relied on home sale prices purchased from Boxwood Means, the share of vacant residential addresses in 2008-2012 

and 2012-2015 purchased from Valassis Lists, and the count of sales of residential properties sold between 2008 and 2014 

purchased from Boxwood Means.

Findings: The association between City County Demolition Project activity and market stability was limited; reduction in vacancy 

was the only measure by which treated neighborhood substantially outperformed comparable areas. The analysis points to a 

limited association between NSP demolitions and housing market conditions. Treated areas experienced large increases in home 

prices, but these increases were matched by comparable areas that did not receive demolitions. At the same time, sales volume, a 

measure of market interest and activity, declined in treated areas, while increasing in comparable areas. 

Table 13: Changes in Treated and Comparable Areas, City County Demolition Project

Treated Areas Comparable Areas

Median Sales Price, 2008 $50,528 $84,889

Median Sales Price, 2014 $94,409 $153,261

Change in Median Sales Price +$43,880 (+87%) +$68,371 (+81%)

Sales Volume, 2008-09 130 23

Sales Volume, 2013-14 79 31
© 2021 Fannie Mae Page 29 of 60

Change in Sales Volume -51 (-39%) +8 (+35%)

% Vacancy, 2008-12 20.7% 7.3%

% Vacancy, 2012-15 10.8% 4.0%

Change in Vacancy -9.9% -3.3%

Another way to understand the trends in treated and comparable areas is to examine how individual treated areas performed 

against their specific comparison block groups. The table below shows the number and proportion of comparable areas that each

treated area outperformed. Viewed this way, it is clear that performance across each of the treated areas was not equivalent. 

Nearly every treated area saw larger declines in vacancy than all of their comparable areas. None of the treated areas saw larger 

increases in sales volume than any of their comparable areas. While half of the treated areas experienced larger home price 

increases than 2 or 3 of their comparable areas, three only outperformed one comparable area and one treated area did not 

outperform any of its comparable areas. 

Table 14: Comparable Areas Outperformed by Treated Areas, City County Demolition Project

Number of Comparable Areas that Treatment Areas 

Outperformed (0, 1, 2, or 3 out of 3)

NIC ID # of Each Treated Area Demolitions,

2010 - 2014

Increases in 

Home Prices

Increase in Sales 

Volume

Decline in 

Vacancy

NIC 6 6 1 0 1

NIC 10 6 3 0 3

NIC 3 8 1 0 3

NIC 2 9 2 0 3

NIC 30 9 1 0 3

NIC 24 10 3 0 2

NIC 27 10 0 0 3

NIC 14 11 3 0 3

# of Comparable Areas Outperformed by All Treated Areas 14 0 21

% of Comparable Areas Outperformed by All Treated Areas 58% 0% 88%

Limitations: One challenge with drawing conclusions from this analysis is that the comparable areas identified in this analysis 

have important differences from treated areas. Home prices were close to 70% greater than in treated areas in 2008, before 

demolitions began, and had much lower vacancy rates in the period before NSP activity. Moreover, some comparison areas were 

in suburban Hennepin County. This analysis relied on comparison areas created for an earlier evaluation of NSP and the data 

collected for those geographies; for this analysis, the available selection of comparison areas were not very similar to those areas 

that had concentrated demolition activity. Access to additional market data would allow for the selection of more similar 

comparison areas. The smaller number of demolitions conducted under the City County Demolition Project, as compared to the 

Kansas City Dangerous Buildings Initiative, may also have limited the team’s ability to detect neighborhood-level changes in 

treated areas. 
© 2021 Fannie Mae Page 30 of 60

Scattered Site Rehabilitation

Intervention Overview: Scattered site rehabilitation, the acquisition and rehabilitation of vacant single-family homes for sale or 

rental, is typically adopted as a strategy to reduce vacancy, create affordable housing, and preserve legacy housing stock. Longterm vacant properties tend to require a gut rehab that may involve an investment of $40,000 or more to make a house viable 

while others will need just minor renovations. Within stronger neighborhood markets, properties have housing resale or rental 

values that can support this level of investment, but weaker markets may require some level of subsidy. One goal for 

rehabilitating properties with subsidy in weaker markets may be to create higher “comparables” that over time will raise housing 

values and create a higher functioning single-family sales market.

Prior Studies Regarding Neighborhood Stabilization Impact: A series of relevant studies have found that the rehabilitation of 

vacant single-family houses for sale or rental positively impacts surrounding property values in every neighborhood market type. 

One study found scattered-site rehabilitation had a greater positive impact on property values than multi-unit new construction 

on a single-site. Studies provide mixed assessments as to whether home improvements are contagious and lead to greater 

investments in surrounding homes. 

Summary of Findings: Neighborhood level associations between market stabilization and scattered site activity were mixed in 

these two non-governmental programs, and there are inherent limits on geographic concentrations in a scattered site model. 

Areas with relative concentrations of West Philadelphia scattered site renovation activity had greater increases in sales prices 

than comparison areas, but on other measures treated neighborhoods and comparable areas saw similar trends. In Dekalb 

County, GA, at a time of widespread sales price and property assessment declines, the decline in home prices was slightly smaller 

in areas around ANDP’s renovated properties and homeownership declines were also slightly less severe. A higher program 

dosage was associated with more notable change; treated areas with the most ANDP renovations were more likely to outperform 

their comparison areas than areas with the fewest ANDP renovations. The limits of the research design do not allow us to extend 

these findings to other types of markets or different versions of a scattered site model.

Note on Quick Basic Policy Analysis: The analyses presented here on these two programs do not examine causal links between 

the interventions and neighborhood stability and cannot be generalized to all local instances of an intervention type. They 

document market changes related to neighborhood stability in areas that were “treated” by a certain intervention.

Program 1: West Philadelphia Scattered Site Model18

 Program description: Two private sector developers, WPRE and Neighborhood Restorations (WPRE/NR) have been 

rehabilitating abandoned rowhouse shells into affordable rental housing in West Philadelphia since 1989. The developers 

purchased long-term vacant rowhouses at below market prices from tax sales and on the private market. From 1989-

2013 the developers produced more than 1,100 units of affordable rental housing in 760 single-family houses or duplexes 

in West Philadelphia. The developers rehabilitated the rowhouses using Low Income Housing Tax Credits (LIHTC) and 

private financing. Over the past three decades, Pennsylvania Housing Finance Agency (PHFA) approved 24 different 

scattered-site redevelopment projects for LIHTC funding. Tenant households who rent these 3-bedroom homes have 

incomes at or below 60% of area median income. WPRE/NR homes are located in some of West Philadelphia’s most 

economically distressed blocks. Wherever possible, WPRE/NR targets their efforts and seeks to acquire and rehab a 

sufficient number of properties to stabilize a block. 

 Relevant state or local law: No change to law needed.

 Duration: 1989 to present

 Goals: Reuse vacant properties for affordable housing and have a significant positive impact on neighborhood 

conditions.

 Agency that administers: For profit developers

 Budget and staffing: One full-time employee to handle vacant house acquisition, LIHTC applications and coordinate 

with developers rehabilitating the properties with a staff budget of approximately $100,000.

18Jim Levin and George Bantel of WPRE/NR provided program description information on March 3, 2021.

3
© 2021 Fannie Mae Page 31 of 60

 Key partners: PHFA, Prime Property Management, an affiliate of WPRE/NR that leases and manages the units

 Funding: LIHTC and private financing

 Key Takeaways

 Maintain rental houses well and they will be indistinguishable from private market homeowners’ houses and help 

create a thriving mixed income neighborhood.

Data Analysis

Overview and Methods: This analysis explores the association between WPRE’s scattered site renovation activity and indicators 

of neighborhood change, comparing census block groups which received concentrations of WPRE renovation activity between 

the years of 2010 and 2014 to other similar census block groups that did not receive WPRE renovation activity. 

The focus was changes in home prices, residential vacancy, and foreclosures between 2010 and 2018. Because the intervention 

occurred across a number of years, in which other factors and macroeconomic trends may have impacted the treated areas the 

team identified comparison areas which had similar characteristics to the treated areas before the intervention. Treated areas 

were defined as block groups where WPRE rehabbed more than three units using LIHTC between 2010 and 2014, and in which 

zero WPRE units were constructed between 2005 and 2009. All of the treated block groups were located in one of the three most 

stressed housing markets identified in the 2011 Philadelphia MVA (G, H, I). The average treated area had 14 LIHTC units built by 

WPRE between 2010 and 2014. A total of 85 units were built in the 6 treated block groups between 2010 and 2014.

For comparable areas, the team identified three block groups for each treated block group that had zero WPRE LIHTC units built 

between 2010 and 2014 and were classified as the same 2011 Philadelphia MVA market category. To further refine the comparable 

areas, the team selected areas with similar median sales prices and foreclosures in 2011. 

The analysis drew on the 2011 and 2018 MVAs, 2010-2011 and 2016-2018 sale records obtained from the City of Philadelphia, 

foreclosure records obtained from the court (2010 to 2011Q1 and 2016 to 2018Q2), and American Community Survey Five Year 

Estimates (2010-14 and 2015-19) for vacancy and tenure. The table below describes the characteristics of treated and comparison 

areas in 2010-2014. All of the areas analyzed were located in an urban area.

Table 15: Characteristics of Treated and Comparable Areas, WPRE Scattered Site

Treated Areas Comparable Areas

Unique Block Groups 6 18

% Black 90% 69%

% White 5% 8%

% Hispanic 0% 17%

% Asian 0% 3%

Housing Units per Square Mile 10,479 8,693

Average Median Household Income $18,547 $24,484

Findings: There were greater increases in sales prices in areas with WPRE renovation activity than in comparison areas. On other 

measures, treated neighborhoods and comparable areas saw similar trends, with little change in foreclosures (slightly up) and

vacancy (slightly down) during this period. While this analysis is not robust enough to identify causal impact, there were 

promising indicators of stabilization in proximity to WPRE’s scattered site renovation activity. Treated areas, on average, 

experienced greater increases in home prices than comparable areas. Changes in foreclosures and residential vacancy were 

similar in treated and comparable areas, with comparable areas experiencing a slightly larger increase in foreclosures, but also a 

larger decline in vacancy than treated areas. 
© 2021 Fannie Mae Page 32 of 60

Table 16: Changes in Treated and Comparable Areas, WPRE Scattered Site

Treated Areas Comparable Areas

Median Sales Price, 2011 $39,442 $38,269

Median Sales Price, 2018 $65,221 $57,109

Change in Median Sales Price +$25,779 (+65%) +$18,839 (+49%)

Average #Foreclosures, 2011 4.5 4.6

Average #Foreclosures, 2018 5.5 6.2

Change in Foreclosures +1.0 +1.6

Average Foreclosures per 100 Housing Units, 2011 0.83 1.20

Average Foreclosures per 100 Housing Units, 2018 1.06 1.55

Change in Foreclosures per 100 Housing Units +0.22 +0.34

Average % Vacant Units, 2010-14 22% 21%

Average % Vacant Units, 2015-19 20% 16%

Change in Vacancy -2% -5%

Another way to evaluate the impact of WPRE’s scattered site activity is to examine how each treated and comparison area’s MVA

classification changed between 2011 and 2018, when the city updated its MVA. The table below shows how the MVA classification

of each block group changed between the 2011 MVA and the 2018 MVA. Every treated area saw an improvement in its MVA 

classification between 2011 and 2015, while only 61% of comparable areas saw an improvement in their MVA classification. 

Table 17: Changes in Market Value Analysis (MVA) Category, Treated and Comparable Areas, WPRE Scattered Site

Declined Stable Improve by 1 

MVA Category

Improved by 2+ 

MVA Categories

Comparable Areas (n = 18) 0% 39% 28% 33%

Treated Areas (n = 6) 0% 0% 17% 83%

Limitations: The small number of foreclosures makes it difficult to draw conclusions from changes in their rate over this time 

period. ACS vacancy data was used because the City of Philadelphia changed the way it estimates vacancy during the course of 

the study period; while the research team has found the City’s new measure more accurate than ACS, there was no comparable 

baseline data. 

Program 2: Atlanta Neighborhood Development Partnership Scattered Site Model19

 Program description: Atlanta Neighborhood Development Partnership (ANDP) launched their scattered site singlefamily rehabilitation program in 2009 in Atlanta. In its first year, ANDP completed a six-home pilot to develop an effective 

process for foreclosed property acquisition, rehab, and rental disposition. With a proven method and capital in hand, 

ANDP competed successfully through city and county-led RFP processes for over $26 million in federal Neighborhood 

Stabilization Program dollars from 2010-16 to acquire, rehab and reactivate vacant, foreclosed single-family homes with 

homeownership. ANDP was active in both the city of Atlanta and in suburban parts of Dekalb and Fulton Counties, which 

19Program description information obtained through an interview with John O’Callaghan, CEO on October 6, 2020 and edited by George Burgan, 

Senior Director for Communications and Technology on February 26, 2021.
© 2021 Fannie Mae Page 33 of 60

each contain a portion of the city. ANDP acquired vacant, abandoned, and foreclosed properties that required $20,000-

$70,000 of rehabilitation to be marketable. ANDP’s goal, was to return affordable homeownership and lift depressed 

home values in neighborhoods most impacted by the foreclosure crisis. ANDP performed most of its scattered site 

rehabilitation work in majority Black neighborhoods with low single-family housing values and high levels of underwater 

properties (the mortgage exceeded the value of the property). ANDP contracted with private sector mission-aligned 

developers and contractors to perform the renovation work with an emphasis on small businesses of color. Newly 

rehabbed homes were offered for sale to income eligible buyers (with overall household incomes at or below 120% of 

AMI initially, evolving into a stronger focus on those below 80% of AMI), including a new emphasis on Veteran and activeduty military homebuyers in 2013. In its initial years, ANDP relied heavily on acquiring properties at low cost from First 

Look Programs as well as donations from local lenders. The NSP program allowed ANDP to provide buyers with down

payment assistance of up to $25,000 in the form of a forgivable loan. ANDP targeted its marketing to partnering real 

estate professionals, mortgage lenders and housing counselors with clients ready for homeownership. 

 Relevant state or local law: None

 Program Duration: 2009 to present

 Goal: To rehabilitate and repopulate single-family homes for low- and moderate-income households throughout the 

metropolitan Atlanta region

 Agency that administers: Nonprofit organization

 Budget and staffing: The budget for the scattered site rehabilitation program grew from $700,000 to almost $15 million 

from 2009 to 2019. ANDP staff rose from 12 to 17 employees.

 Key partners: Mission-aligned private sector contractors and developers who perform rehab and identify properties,

county, and city governments, Neighborworks America who provided training, and National Community Stabilization 

Trust and private lenders who provided REO properties.

 Funding Sources: From 2008-2013, ANDP relied heavily on NSP1 and 3, winning over $26 million in NSP funding. In 2013, 

ANDP became a Community Housing Development Organization (CHDO) in several local jurisdictions to access over $3 

million in federal HOME funds for foreclosure redevelopment. ANDP also raised private enterprise-level capital, 

philanthropic grants, Program Related Investments from banking and finance sector, and public capital through 

programs such as New Markets Tax Credits (NMTCs) and Capital Magnet Fund resources (CMF).

 Key Takeaways

 The federal response to the foreclosure crisis, including First Look programs and NSP, created opportunities to pilot 

a new scattered site acquisition and rehabilitation model. The model, however, has proven sustainable with creative 

and aggressive raising of enterprise capital, New Market Tax Credits, Capital Magnet Fund resources and other 

sources of funding. 

 In low value neighborhoods, a key to reviving markets is creating new comparable sales transactions to raise values 

sufficiently to allow for profitable rehabilitation of older homes. 

Data Analysis 

Overview and Methods: The research team focused on suburban areas as an opportunity to evaluate a neighborhood 

stabilization effort conducted outside of a central city, where such efforts are often implemented. To evaluate the association 

between ANDP’s scattered site renovation activity in suburban portions of Dekalb County and changes in the surrounding 

housing market, the team analyzed changes in home prices and sales volume between 2009 and 2015, changes in assessed values 

between 2010 and 2016, changes in home ownership between 2007-11 and 2015-19, and changes in foreclosure filings between 

2008-09 and 2015-16. This analysis only examines renovated homes that were sold to homeowners. The average treated block 

group experienced 6.25 ANDP renovations between 2010 and 2014. The treated block group with the greatest number of ANDP 

renovations experienced 8 between 2010 and 2014.

The team selected comparison areas which had similar characteristics to the treated areas before the intervention. It is important 

to note that over the period in which ANDP was doing its work, housing prices in DeKalb County were falling. Estimates from 

Zillow indicate that home values fell nearly 10% between 2011 and 2012. Over the same period, assessed property values in the
© 2021 Fannie Mae Page 34 of 60

county were also declining across the county. Treated areas were defined as block groups where ANDP performed more than four 

renovations between 2010 and 2014. Each treatment area was matched with 3 block groups in DeKalb County with similar home 

sales prices in 2009, homeownership rates in 2007-11, foreclosure filings in 2008-09, and no ANDP renovations. All of the areas 

analyzed were located in suburban areas. 

Data sources were residential home sales occurring in 2009 and 2015, purchased from Data Axle, average total assessed values for 

residential properties in tax years 2010 and 2016 downloaded from the DeKalb County Assessor’s Office (which reassesses 

properties annually), residential foreclosure filings in 2008-09 and 2015-2016 purchased from Attom Data, and tenure from the 

ACS Five Year Estimates, 2007-11 and 2015-19.

Table 18: Characteristics of Treated and Comparable Areas, ANDP Scattered Site

Treated Areas Comparable Areas

# Block Groups 4 12

% Black 94% 93%

% White 2% 3%

% Hispanic 2% 1%

% Asian 0% 1%

Percent Other 1% 2%

Housing Units per Square Mile 557 539

Average Median Household Income $62,445 $56,787

Findings: Both treated and comparison areas experienced a decline in housing values between 2008 and 2014. The decline in 

home values was slightly larger in comparable areas, where values fell by 6% on average, compared with only 3% in treated areas. 

The number of non-distressed sales that occurred in each block group increased in both treated and comparison areas, with 

comparable areas experiencing a much larger increase in sales volume on average. Assessed values in treated and comparable 

areas experienced similar declines in assessed values. On average, treated neighborhoods saw assessed values fall by 20%, while 

comparable areas saw values decline by 21% between 2010 and 2016. 

Treated neighborhoods saw a larger decline in foreclosures than comparable areas between 2008-09 and 2015-16. On average, 

treated areas experienced a decline of 2.8 foreclosures per 100 households, while comparable areas experienced a decline of 1.1 

foreclosures per household. Treated and comparable areas experienced similar declines in homeownership, (declining by 8% on 

average in treated areas and 11% in comparable areas). 

Table 19: Changes in Treated and Comparable Areas, ANDP Scattered Site

Treated Areas Comparable Areas

Median Sales Price, 2008 $142,375 $133,583

Median Sales Price, 2014 $138,375 $125,417

Change in Median Sales Price -$4,000 (-3%) -$8,167 (-6%)

Average Sales Volume, 2008 29 17

Average Sales Volume, 2013 32 27

Change in Sales Volume +2 (+10%) +9 (+59%)

Average Assessed Value, 2010 $150,631 $129,058

Average Assessed Value, 2016 $120,869 $101,826
© 2021 Fannie Mae Page 35 of 60

Change in Average Assessed Value -$29,763 (-20%) -$27,232 (-21%)

Average Foreclosure Filings, 2008-09 178 137

Average Foreclosure Filings, 2015-16 37 25

Change in Foreclosure Filings -141 (-79%) -112 (-82%)

Average Foreclosure Filings per 100 Households, 2008-09 3.5 1.3

Average Foreclosures per 100 Households, 2015-16 0.7 0.2

Change in Foreclosure Filings per 100 Households -2.8 -1.1

Average Ownership Rate, 2007-11 89% 85%

Average Ownership Rate, 2015-19 81% 74%

Change in Ownership Rate -8% -11%

Another way to understand the trends in treated and comparable areas is to examine how individual treated block groups 

performed against their specific comparison areas. The table below shows the number and proportion of comparable areas that 

each treated area outperformed. Viewed this way, it is clear that performance across each of the treated areas was not 

equivalent. Treated areas with the fewest ANDP renovations were less likely to outperform their comparison areas than treated 

areas with the most ANDP renovations. Nearly all treated areas outperformed all of their comparison areas in terms of foreclosure

filings. 

Table 20: Comparable Areas Outperformed by Treated Areas, ANDP Scattered Site

Number of Comparable Areas that Treatment Areas Outperformed 

(0, 1, 2, or 3 out of 3)

Block Group ID # of Each 

Treated Area

Renovations, 

2010 - 2014 Home Prices Sales Volume Ownership Foreclosure Filings

Assessed 

Values

130890234241 5 1 2 2 3 3

130890234184 6 0 0 2 3 0

130890233121 7 2 0 3 2

130890233122 8 3 3 1 3 0

# of Comparable Areas Outperformed by All 

Treated Areas 6 5 8 11 5

% of Comparable Areas Outperformed by All 

Treated Areas 50% 42% 67% 92% 42%

The team also analyzed changes in assessed property values among homes within 500 feet of an ANDP renovation and compared 

them to the change in assessed values for homes located in the same block group but farther than 500 feet from an ANDP 

renovation. The table below shows the average assessed value of homes in the year prior to each ANDP renovation and three 

years after each ANDP renovation. 

Homes near ANDP renovation experienced greater increases in assessed values than homes farther away from ANDP renovations. 

For example, among homes near an ANDP renovation in 2013 assessed values grew by 79%, while homes in the same block group 

that were not located near an ANDP renovation saw assessed values grow by only 63% over the same time period.
© 2021 Fannie Mae Page 36 of 60

Limitations: Multiple foreclosure prevention programs were operating widely during the period of analysis, both in treated and 

comparison areas. The team did not evaluate factors such as new construction activity that may be associated with changing 

assessed values. 

Table 21: Changes in Treated and Comparable Buffer Areas, ANDP Scattered Site

Renovation 

Year Study Period ANDP Renos

Properties within 500-Feet of an 

ANDP Renovation (Reno)

Properties More than 500-Feet from an 

ANDP Renovation

As. Value 

Pre-Reno

As. Value 

Post-Reno

%. 

Change

As. Value 

Pre-Reno

As. Value 

Post-Reno %. Change

2012 2011 to 2015 13 $106,693 $102,026 -4% $123,782 $114,433 -8%

2013 2012 to 2016 13 $48,651 $87,085 79% $60,173 $97,791 63%

2014 2013 to 2017 6 $48,731 $100,505 106% $57,926 $104,630 81%

2015 2014 to 2018 8 $63,606 $124,437 96% $97,290 $170,261 75%

2016 2015 to 2019 17 $79,244 $127,442 61% $76,479 $120,333 57%

2017 2016 to 2020 8 $98,407 $150,838 53% $97,494 $149,154 53%

Total 65 $76,232 $114,311 50% $86,114 $122,887 43%

Land Bank 

Intervention Overview: Over 170 counties and municipalities across the country have formed land banks to reactivate vacant 

and underutilized properties and return them to productive use.20 Typically created pursuant to state enabling legislation, some 

land banks are created as a local government agency, while others are formed as quasi-governmental agencies or independent 

nonprofits. The powers of land banks differ across the country, but a key purpose land banks serve is to convert tax-delinquent 

properties back into productive reuse. As a result, many land banks, including the two evaluated here, have some power to 

acquire tax foreclosed properties outside of tax sale auction to proactively transfer the property to an eligible buyer with the 

interest, experience, and financial capacity to reuse or redevelop the property. 

Prior Studies Regarding Neighborhood Stabilization Impact: Studies find that auctions of tax-foreclosed properties to the 

highest bidder often result in the property remaining vacant, unmaintained, and tax delinquent. Land banks with the power to 

obtain tax foreclosed properties outside of the tax auction process and transfer properties to responsible buyers achieved better 

outcomes, including reactivating the property with stable ownership and increasing property values. Studies suggest that the 

transfer of a property to a land bank may preserve the value of nearby homes, but it does not increase property value unless the 

property is well-maintained or renovated. 

Summary of Findings: In both cities, areas with land bank activity also had signs of neighborhood stabilization. In Houston, land 

bank activity was concentrated in three market types all on the weaker end of the city’s markets and all with low median 

incomes. The most notable stabilization occurred in the strongest of the three markets. These markets also had the greatest 

concentration of land bank activity. In Albany, land bank activity was exclusive to the city’s most stressed low-income markets, 

making the identification of a non-treated comparison area difficult. Differences did emerge in looking at areas with one or no 

land bank transactions and those with two or more; there was a larger increase in sale prices and decreasing vacancy in treated 

areas with more transactions. Similar to the code enforcement interventions, market context and program dosage may have 

played a role in these program locations.

20Frequently Asked Questions on Land Banking, Community Progress https://communityprogress.org/resources/land-banks/lb-faq/.

4
© 2021 Fannie Mae Page 37 of 60

Note on Quick Basic Policy Analysis: The analyses presented here 

on these two programs do not examine causal links between the 

interventions and neighborhood stability and cannot be 

generalized to all local instances of an intervention type. They 

document market changes related to neighborhood stability in 

areas that were “treated” by a certain intervention.

Program 1: Houston Land Bank21

 Program description: The Houston Land Bank is a quasipublic local government corporation that obtains vacant, 

abandoned, and deteriorated properties and transfers 

them to new responsible owners for new construction that 

meets community needs such as quality affordable housing. 

The land bank solely acquires vacant parcels without structures and sells these properties to relatively small for-profit 

and nonprofit builders for the purpose of constructing single-family homes. The deed requires the builders to construct 

affordable homes that maintain their affordability for ten years. From 2002 to 2017, the land bank (known until 2018 as 

the Land Assemblage Redevelopment Authority (LARA)) acquired tax sale properties through direct purchase or as “strike 

off” that failed to sell at auction from the county. Properties that did not sell were deeded to the land bank for closing 

costs through an agreement with taxing authorities that expired in 2017. The land bank was run as a city program with 

shared staff from 1999 to 2017. Only in 2018 did the authority officially change its name to the Houston Land bank and 

hire its first CEO and independent staff. 

 Relevant state or local law: Subtitle A, Title 12, Local Government Code, Chapter 379H Urban Land Bank Program in 

Municipality with Population of Two Million or More (2021).

 Duration: Incorporated as the Land Assemblage Redevelopment Authority (LARA) in 1999 and became the Houston Land 

Bank in 2018.

 Goal: To reclaim vacant, abandoned, and deteriorated vacant parcels on behalf of the City that meets community needs 

for quality affordable housing, community, and economic development and increased quality of life

 Agency that administers: Houston’s Housing and Community Development Department is responsible for overseeing 

the initiative and is the sponsoring agency. The land bank is an independent corporation with its own board of directors 

appointed by the Mayor, City Council, the county, and the School District.

 Budget and staffing: The land bank operated within the Housing and Community Development Department, borrowing 

staff time and spending down on direct program dollars until 2018. During the five years from 2010-2015, the land bank 

spent approximately $1.5 million on property maintenance and $2 million on land purchase including closing costs, 

permits, and property taxes. 

 Geography: Land Bank focuses on nine historically neglected Houston neighborhoods that have high concentrations of 

vacant abandoned, tax delinquent lots.

 Key partners: City of Houston, homebuyers of new construction affordable housing, non- and for-profit builders such as 

Habitat and in recent years, the Houston Community Land Trust

 Funding: The majority of funding comes from a central pool of local tax increment financing dollars derived from a Tax 

Increment Reinvestment Zone (TIRZ), a special district that allows investments in public improvements to be made from 

the resulting tax increases. The TIF dollars are dedicated to funding affordable housing with a mandatory 10-year 

compliance period. Currently, the land bank also earns over $1 million annually in sales and administrative fees. 

21Program description information obtained through interview with Anne Gatling Haynes, Houston Land Bank CEO on January 15, 2021.

Differences did emerge in looking 

at areas with one or no land bank 

transactions and those with two 

or more; there was a larger 

increase in sale prices and 

decreasing vacancy in treated 

areas with more transactions.
© 2021 Fannie Mae Page 38 of 60

 Key Takeaways

 The land bank requires a full-time director and staff. From 1999 to 2018, the land bank capacity was limited because 

it shared staff with its supervising city agency.

 In a growing city like Houston, CEO and President Anne Gatling Haynes says vacant land is currency, and there is a 

power in ensuring that vacant land is redeveloped for purposes that serve the community. 

 Acquiring land at low prices is critical to a land bank model. Yet in the recent competitive real estate market, less 

than 5% of tax sale properties fail to sell. Now that the interlocal agreement has expired, the land bank must create a 

new model for cost-effectively acquiring and transferring properties to responsible buyers.

 Good data collection and analysis helps to ensure buyers use land for intended purposes, such as affordable 

housing, and to measure impact.

 Vacant properties sold to adjacent owners will often remain vacant without a committed new owner.

 Early on, the land bank sold vacant parcels for $1 when it was difficult to sell lots in disinvested neighborhoods but 

found that builders prioritized the redevelopment of land for which they paid a market value purchase price. As a 

result, the $1 lot program ended in 2018. Now, the land bank sells parcels at a discounted market value of 

approximately $10,000 to $20,000 per lot depending on the neighborhood.

Data Analysis 

Overview and Methods: The goal of this analysis is to understand how land bank acquisition and subsequent sale of vacant land 

impacted home sale prices in Houston, TX. The land bank activity examined in this analysis occurred between 2010 and 2019. To 

understand how these activities impacted the local housing market, the team analyzed changes in home sales price between 

2010/2011 and 2018/2019. Block groups in which the land bank acquired and resold at least five properties were considered part 

of the treatment group. A set of block groups which were in the same MVA category in 2013, but did not have land bank 

acquisitions, were selected as comparisons.

Table 22: Characteristics of Treated and Comparable Areas, Houston Land Bank

2013 MVA F 2013 MVA G 2013 MVA H

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

# Block Groups 3 4 2 3 6 13

Median Income $19,300 $35,000 $19,600 $17,800 $22,800 $25,800

Housing Units per 

Square Mile 1,620 1,440 2,200 1,500 1,200 1,700

% Black 69% 81% 89% 76% 67% 75%

% White 0% 1% 1% 0% 5% 2%

% Hispanic 31% 16% 10% 23% 27% 23%

% Asian 0% 2% 0% 0% 0% 0%

Property sales price records were purchased from InfoUSA (now Data-Axle). Reinvestment Fund’s 2016 MVA classified the lowincome treated areas as F, G, and H on a citywide market scale from A (strongest, e.g., higher sales prices, lower vacancy) to I 

(most distressed, e.g., lower sales prices, higher vacancy). The treated areas were all low-income. The average treated block 

group in the MVA F category experienced 49 land bank acquisitions and resales between 2010 and 2019. Treated block groups in 

MVA category G experienced an average of 36 land bank sales, and block groups in MVA category H experienced 22 land bank sales 

on average.
© 2021 Fannie Mae Page 39 of 60

Table 23: Changes in Treated and Comparable Areas, Houston Land Bank

2013 MVA F 2013 MVA G 2013 MVA H

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

Treated 

Areas

Comparable 

Areas

# Block Groups 3 4 2 3 6 13

Average # Land Bank Sold Prop. 49 0 36 0 22 0

Average # of Sales, 2010/2011 56 54 35 17 87 149

Average # of Sales, 2018/2019 125 82 60 39 123 277

Median Sale Price, 2010/2011 $85,300 $91,000 $80,800 $91,500 $57,300 $49,100

Median Sale Price, 2018/2019 $164,000 $95,800 $115,000 $150,800 $106,000 $93,200

% Sale Price Change, 2010/2011-

2018/2019 95% 17% 42% 57% 95% 90%

Findings: The land bank treated block groups in MVA category F outperformed comparable areas in residential sale price change 

by the largest amount, with an average 95% increase in sales price in land bank treated areas compared to 17% in areas without 

land bank activity. F is the strongest market type out of the three categories of treated areas, and F block groups also had the 

highest amount of land bank activity. Land bank treated block groups in MVA category G underperformed comparable block 

groups (42% to 57%), and land bank treated block groups in MVA category H slightly outperformed comparable areas (95% to 

90%).

Limitations: One limitation of this analysis is the lack of data on what happened to properties after the land bank resold the 

property to the private market. Some sales may have led to development of new housing while others may remain vacant lots. 

The subsequent choices of private developers likely affect local sale prices. Additionally, the number of block groups with land 

bank activity was small. Another limitation is that Reinvestment Fund has not completed an MVA in Houston since 2016, and so 

more comprehensive data on how other market factors like vacancy, foreclosure filings, building permitting, and overall market 

strength have changed in the examined block groups was not readily available.

Program 2: Albany County Land Bank22

 Program description: The nonprofit Albany County Land Bank was established in 2014 to facilitate the process of 

acquiring, improving, and transferring tax-foreclosed, vacant, or abandoned properties to productive use. The county 

provides almost all of its tax foreclosed properties to the land bank at no charge after extinguishing back taxes and liens 

on the properties. More than half of the properties acquired by the land bank are vacant lots without a structure. Where 

there is a structure, the land bank establishes an action plan based upon the condition of the structure and whether it is 

ready to be sold immediately, requires some level of stabilization prior to sale, requires rehabilitation that exceeds the 

market value of the property, or must be demolished. The land bank accepts purchase offers for properties on an open, 

competitive basis. In order to purchase a property, buyers cannot have a history of negative or harmful real estate 

stewardship and must provide a plan and proof of financial capacity to complete the proposed project. As part of the 

purchase process, the land bank requires all owners to “put their feet” in the building to understand how much work is 

needed. Approved buyers must also sign a redevelopment agreement committing to use the property for authorized 

purposes and/or complete rehabilitation within a specified timeframe. Where the owner fails to do so, the land bank can 

recapture the property through various methods. Thanks in part to the purchase process, the land bank has not had to 

take action to recapture a property to date. Properties are typically turned around within 12 months.

22Program description information obtained through interview with Adam Zaranko, Executive Director on January 11, 2021.
© 2021 Fannie Mae Page 40 of 60

 Relevant state or local law: Enabled under the 2011 Land Bank Act, Article 16 of the New York State Not-for-Profit 

Corporation Law and Albany County Resolution 68 of 2014

 Duration: 2014 to present

 Goals: Facilitate the process of acquiring, improving, and redistributing vacant properties, eliminate the harms & 

liabilities caused by such properties, return properties to productive use, and remain consistent with the municipality’s 

redevelopment and comprehensive plans

 Agency that administers: 501(c)(3) Nonprofit

 Budget and staffing: $2.5 million budget in FY21 and 8 FTE staff members

 Key partners: Albany County, municipal governments, nonprofits including organizations like the Albany Community 

Land Trust and Habitat for Humanity, private buyers, and responsible real estate developers and investors

 Funding: Funding from the New York State Attorney General’s Office and Enterprise Community Partners, Inc, Albany 

County, and the City of Albany, along with sales proceeds and associated fees. There are no NYS Attorney General grant 

funds that will extend beyond FY 2021.

 Key Takeaways

 The land bank’s ability to obtain a large volume of tax foreclosed properties at differing market values in a variety of 

municipalities helps provide operating income that funds a significant portion of its operations. 

 A land bank needs grants to reclaim or demolish properties in weak market neighborhoods where there is no 

demand for properties. As executive director Adam Zaranko says, “we receive 100-plus-year-old properties, and now 

we hold the bag. Grant money is what we use to fill the bag.”

 Access to capital for buyers is a significant challenge in neighborhoods that have faced decades of lender 

disinvestment. Only 12 of the 275 buyers of land bank properties were able to obtain loans to finance the purchase 

and rehabilitation of the properties from traditional lenders. As a result, neighborhood residents who are eligible 

buyers are turned down because they cannot raise the purchase price plus money for critical rehabilitation costs. 

The land bank is creating a new partnership with a community lender in Syracuse, Home HeadQuarters, to ensure 

more residents can buy properties in their neighborhoods and reduce Albany’s significant gap between white and 

Black homeownership.

 The land bank primarily has access to tax foreclosed properties. The land bank has no power to revitalize 

surrounding vacant and abandoned properties that are bank owned or privately owned.

 The land bank works in the neighborhoods with so many other good programs that it may be hard to isolate the land 

bank’s work. 
© 2021 Fannie Mae Page 41 of 60

Data Analysis 

Overview and Methods: The goal of this analysis is to 

understand how clustered land bank acquisition and resale

activity of foreclosed properties impacted sale prices and 

vacancy in Albany, NY. The land bank activity examined in 

this analysis occurred between 2015 and 2020. To 

understand how these land bank activities impacted the 

local housing market, the team analyzed changes in home 

prices between 2012/2014 and 2018/2020 and changes in the 

share of vacant properties between 2013 and 2020. Census 

block groups with at least two properties that the land bank 

had both acquired and resold were considered treated areas. 

Block groups with no land bank activity from 2015 through 

2020 and median sale prices below $150,000 in 2012/2014 

were considered comparable areas. 

The average treated block group experienced 11 land bank 

acquisitions and dispositions between 2015 and 2020. The treated block group with the greatest number of land bank 

acquisitions and dispositions experienced 32 between 2015 and 2020. 

Residential property sales were downloaded from albany-ny.tolemi.com for 2012-2020. The City of Albany provided lists of 

parcels cited for vacancy for 2013, 2017, and 2020.

Table 25: Changes in Treated and Comparable Areas, Albany County Land Bank

Treated Areas Comparable Areas

Average # Land bank Properties Sold 11 0

Median Sales Price, 2012/2014 $68,800 $132,400

Median Sales Price, 2018/2020 $93,500 $162,200

% Change in Median Sales Price 40% 20%

% Vacant Residential Prop., 2013 7.2% 1.1%

% Vacant Residential Prop., 2020 7.0% 2.3%

Change in Vacant Residential Prop. -0.2% 1.3%

Prior to 2016, the Land Bank sold fewer than 10 properties per year but has sold at least 20 per year since. The median length of 

time a property remained in the land bank before sale was 364 days.

Treated Areas Comparable Areas

# Block Groups 30 13

% Black 48% 22%

% White 28% 56%

% Hispanic 13% 11%

% Asian 6% 7%

Med. HH Income $32,200 $50,800

Housing Units per 

Square Mile 3,100 3,000

Table 24: Characteristics of Treated and Comparable 

Areas, Albany County Land Bank
© 2021 Fannie Mae Page 42 of 60

Table 26: Count and Median Sale Price 2012-2020, Albany County Land Bank

 

Findings: Block groups with land bank treated properties experienced a much larger increase in sale prices (as a percentage) 

than comparable areas (40% to 20%), although they also began the period with lower sale prices. The vacancy rate decreased 

slightly in land bank treated areas (-0.2%) but increased in comparable areas (1.3%) over the same time period. The areas began 

and ended with higher vacancy rates, however.

Limitations: The land bank works comprehensively in the most distressed parts of Albany, making it difficult to find strictly 

comparable areas. The Code Enforcement Division indicated that their methods for citation of vacant parcels, especially 

buildings, has improved greatly in recent years. The changes in observed vacancy levels, therefore, could reflect improved 

enforcement practices rather than actual changes in residential vacancy. 

First Look Program 

Intervention Overview: First Look programs were developed in response to the Great Recession to give owner occupants and

local nonprofit buyers a “first look” to purchase single-family real-estate-owned (REO) properties in the portfolios of financial 

institutions. The goal of First Look programs is to promote homeownership and allow local nonprofits seeking to stabilize 

neighborhoods to negotiate and purchase foreclosed properties before they are made available to investors that will help to 

achieve their mission. The National Community Stabilization Trust (NCST) was founded in 2008 by six national nonprofits to 

facilitate sales of single-family REO properties to a network of vetted, community-based, nonprofit, and mission-focused forprofit organizations and land banks. NCST facilitated approximately 17,000 First Look property sales between 2008 and 2020 on

behalf of the Federal Housing Administration, Fannie Mae, Freddie Mac, and a number of large banks and mortgage servicers. 

Under First Look programs, eligible non-owner occupant buyers are given a short period of time to express interest (i.e., 48 hours) 

and then a slightly longer period (i.e., 15 days) to perform inspections and evaluate whether to buy a property. Prices are set by 

the seller based upon a cost avoidance strategy where the seller avoids certain costs by transferring the property through the 

program, such as maintenance and marketing. In situations where no owner occupants or local nonprofits choose to buy a 

property, the property is made available for sale through retail disposition processes such as the Multiple Listing Service or 

auction websites. The vast majority of First Look sale properties were rehabbed and sold for homeownership.

Prior Studies Regarding Neighborhood Stabilization Impact: Limited studies of First Look Programs that seek to sell singlefamily REO properties to nonprofits and local owner-occupant buyers rather than investors found that they may reduce negative 

impacts on neighborhoods, including declining property values and risk of future tax foreclosure. A recent paper argues there 

should be no direct link between sales prices and the First Look program, but rather through changing the buyer composition to 

investors.23

23Leaving Households Behind: Institutional Investors and the U.S. Housing Recovery by Lauren Lambie-Hanson, Wenli Li, Michael 

Slonkosky : SSRN.

Year Count of Land Bank Sales Median Sale Price of Land Bank Sales

2012 7 $5,000

2013 3 $0

2014 5 $0

2015 3 $1

2016 24 $750

2017 35 $14,500

2018 40 $10,000

2019 33 $5,100

2020 38 $7,000

5
© 2021 Fannie Mae Page 43 of 60

Summary of Findings: Analysis found observable market stabilization trends in areas within 500 feet of First Look properties in 

Essex County, NJ, but less clear results in Cook County, IL. Proximity to a First Look home in Essex County was associated with a 

larger decline in bank sales, a larger increase in median sales price, and an increase in sales volume. Proximity to a First Look 

home in Cook County was associated with an increase in median sales price and smaller decrease in sales volume relative to the 

comparison area, but also with an increase in foreclosure filings. It is important to note that while these analyses focus on market 

changes within a small area, First Look programs generally do not amass large number of properties within smaller, targeted 

geographies. As noted in the analyses of emergency bridge loans that follow, previous research has demonstrated impacts of 

individual foreclosed properties; this quick basic policy analysis did not capture all of the potential effects of the program.

Note on Quick Basic Policy Analysis: The analyses presented here on these two programs do not examine causal links between 

the interventions and neighborhood stability and cannot be generalized to all local instances of an intervention type. They 

document market changes related to neighborhood stability in areas that were “treated” by a certain intervention.

Program 1: New Jersey Community Capital24

 Program description: In 2010, New Jersey Community Capital (NJCC), a Community Development Financial Institution 

(CDFI), began to acquire Northern New Jersey vacant and foreclosed properties from the National Community 

Stabilization Trust (NCST) First Look mortgage pools through its newly acquired subsidiary, the Community Asset 

Preservation Corporation, (CAPC). Over time NJCC acquired 143 properties from NCST, with 127 of those being singlefamily homes. NJCC acquired these properties both individually and as part of 18-20 bulk property pools between 2010 

and 2016. CAPC staff evaluated thousands of First Look REO properties and purchased a small percentage of those 

evaluated based upon capital availability and options for disposition and reuse in target markets. CAPC has continued to 

build on the First Look Program, and in FY 20, sold 169 renovated owner-occupied properties to buyers with an average 

income of 98% of AMI. 63% of these sales were to first time homebuyers and 68% were to minority households. CAPC has 

a rental portfolio consisting of 591 units leased to tenants with an average income of 43% of AMI. CAPC targeted its 

efforts by obtaining a critical mass of properties in distressed neighborhoods where NJCC was already working using a 

multipronged stabilization approach that included initiatives to use resident engagement and cross sector partnerships 

to improve public spaces and educational opportunities.

 Relevant state or local law: No change to law needed.

 Duration: 2010 to present

 Goals: Acquire foreclosed, real-estate-owned (REO) properties to stabilize fragile neighborhoods and protect 

homeowners and tenants from the toxic effects of the foreclosure crisis

 Agency that administers: Nonprofit Community Development Financial Institution (CDFI)

 Budget and staffing: Staff of 19 in FY20. Total income has grown from $319,000 in FY11 to $10.9 million in FY20. 

 Key partners: Lenders who provided loans, lines of credit, and grant assistance.

 Funding Sources: Lines of credit (largest was $13 million with Prudential (ASF 1) and acquisition and construction loans. 

In addition, by FY 20, the properties earned approximately $4.3 million in rent. A 5% construction management fee also 

helped finance acquisition of new properties.

 Key Takeaways

 Neighborhood change takes time. It requires an investment of ten years or more to move the needle and improve a 

neighborhood.

 Reactivating vacant and foreclosed properties requires access to flexible capital in the forms of low interest loans 

and lines of credit in addition to project-based subsidy.

24Program description information obtained through an interview with Wayne Meyers, President and Jeff Crum, Chief Investment Officer of 

NJCC on October 6, 2021.
© 2021 Fannie Mae Page 44 of 60

Data Analysis

Overview and Methods: The analysis examined the changes in bank sale volume (2013-16 to 2017-19), median sales prices (2013-

16 to 2017-19), and residential sales volume (2013-16 to 2017-19) in the study and comparison areas. Because the treated and 

non-treated areas were parts of the same census tract, the demographic characteristics were the same. Treated areas were 

defined as the portion of a census tract within a 500-foot buffer of a First Look property between 2013 and 2016 with the rest of 

the census tract outside the 500-foot buffer considered the comparable areas. There were 68 First Look properties in Essex 

County, New Jersey, between 2013 and 2016.

Table 27: Characteristics of Treated and Comparable Areas, First Look Essex County, NJ

Treated and Comparable Areas

# Census Tracts 137

Household Income $54,745

Housing Units per Square Mile 3,524

% Black 50.6%

% White 17.1%

% Hispanic 26.2%

% Asian 3.4%

The National Community Stabilization Trust (NCST) and New Jersey Community Capital provided the First Look program data. 

Demographic and income data came from the U.S. Census Bureau’s American Community Survey 2015-19 5-year estimates. Sales 

volume and median home sales prices came from Monmouth County’s Open Public Records Search System (OPRS). Bank sales 

were estimated based on a Reinvestment Fund analysis of the OPRS sales data. 

Findings: Proximity to a First Look home was associated with a larger decline in bank sales, a larger increase in median sales 

price, and an increase in sales volume. Both treated and comparison areas saw declines in the number of bank sales, but the 

decline was larger for areas close to First Look properties (-50.9% vs. -40.6%). Both areas also saw an increase in median sales 

price, but the gains were larger for the treated area (43.4% vs. 26.4% increase). Sales volume also increased in both the study and 

comparison areas, but the increase was larger in the treated area (40.5% vs. 20.5%). 

Table 28: Changes in Treated and Comparable Areas, First Look Essex County, NJ

Treated Areas Comparable Areas

# Census Tracts 137 137

# Bank Sales, 2013-16 57 106

# Bank Sales, 2017-19 28 63

Change in Bank Sales -50.9% -40.6%

Average Median Sales Price 2013-16 $133,656 $216,643

Average Median Sales Price 2017-19 $191,712 $273,878

Change in Average Median Sales Price 43.4% 26.4%

# Sales, 2013-16 4,780 13,138

# Sales, 2017-19 6,715 15,827

Change in Sales Volume 40.5% 20.5%
© 2021 Fannie Mae Page 45 of 60

Limitations: The design of this intervention focused on foreclosed properties available through the First Look program acquired 

by just one buyer, wherever they were located. As a result, there were not significant clusters of activity. At the same time, other 

First Look houses may have been sold in the treated/not treated areas, although NJCC was the most active buyer. 

Program 2: Community Investment Corporation Community Initiatives Acquisition Program -

Chicago25

 Program description: The Community Investment Corporation (CIC) through its affiliate Community Initiatives, Inc. (CII), 

is one of the leading buyers of NCST First Look properties in Chicago. In 2013, responding to widespread abandonment of 

Chicago’s struggling 1-4 unit rental housing stock caused by the foreclosure crisis, CIC launched the Community 

Initiatives Acquisition Program. The mission of the new program was to acquire distressed 1-4 unit housing in low- and 

moderate-income neighborhoods at below market prices. CIC then sold these properties to vetted private-market 

developers for immediate renovation and sale for homeownership or rental. CIC bought properties for which the total 

cost of acquisition and rehab could be supported by neighborhood rents or sales prices. CIC acquired just 9 properties in 

2014 and expanded to 659 properties by the end of 2020. The majority of the First Look properties CIC acquired were 

single family homes. Once CIC bought the property and the closing process was complete, CIC quickly sold the property 

to a vetted developer and recycled the money back into their acquisition fund. CIC monitored the renovation of each 

property and encouraged the developers to renovate at a high-quality level and to obtain the highest price possible for 

the housing. Developers were not permitted to simply hold the property or to “flip” it to another investor. By 2016, home 

values were high enough in many neighborhoods to sell the properties for homeownership, which allowed the small 

developers to immediately reinvest their proceeds into a subsequent property. 

 Relevant state or local law: No change in law needed.

 Duration: 2013 to present

 Goals: Preserve and improve unsubsidized affordable rental and homeownership housing units and return them to the 

tax rolls without subsidy

 Agency that administers: Nonprofit Community Development Financial Institution (CDFI)

 Budget and staffing: 2-5 person staff team. Annual budget is approximately $823,000 in 2021.

 Key partners: Small reliable developers who renovated the properties and NCST’s First Look Program that provided 

distressed properties at below market prices.

 Funding Source: In 2013, CIC received a $5 million PRO Neighborhoods grant from the JPMorgan Chase Foundation to 

create a 1-4 housing units collaborative effort with Neighborhood Housing Services of Chicago (NHS) and Chicago 

Community Loan Fund (CCLF) that funded the acquisition program. CIC recycled acquisition dollars several times and 

charged an acquisition and management fee for properties where the sales price could support it.

 Key Takeaways

 Ensure staff understands private market real estate development.

 Collaborate with private market developers who are rehabilitating properties for a profit.

 Help a dependable set of small developers get to scale by providing a dependable pipeline of properties.

Data Analysis

Overview and Methods: To examine the relationship of First Look activity and market stabilization in Cook County, the team 

applied the same strategy as the in Essex County, NJ, examining the changes in bank sale volume (2013-16 to 2017-19), median 

sales prices (2013-16 to 2017-19), and residential sales volume (2013-16 to 2017-19) in treated and comparison areas. Treated 

areas were defined as the portion of a census tract within a 500-foot buffer of a First Look property between 2013 and 2016, with 

25Program description information obtained through an interview with Andre Collins, VP Director of Acquisitions on February 24, 

2021.
© 2021 Fannie Mae Page 46 of 60

the rest of the census tract outside the 500-foot buffer considered the comparable areas. Based on their density, 628 census tracts 

were categorized as urban core, 288 were categorized as suburban. There were 1,647 First Look properties purchased by NCST 

buyers in Cook County, Illinois between 2013 and 2016.

NCST provided the First Look program data. Demographic and income data came from the U.S. Census Bureau’s American 

Community Survey 2015-19 5-year estimates. Sales volume and median home sales prices came from the Cook County Assessor’s 

Office. Foreclosure filing data from 2013 to 2015 came from the Cook County Recorder of Deeds’ Office. Foreclosure data from 

2017 to 2019 was purchased from ATTOM Data Solutions. 

Table 29: Characteristics of Treated and Comparable Areas, First Look Cook County, IL 

Treated and 

Comparable Areas

# Census Tracts 916

Average Median Household Income, 2015-19 $58,443

Housing Units per Square Mile 2,041

% Black 28.9%

% White 34.8%

% Hispanic 28.8%

% Asian 5.6%

Findings: Proximity to a First Look home in Cook County was associated with an increase in median sales price and a smaller 

decrease in sales volume relative to the comparison area, but also with an increase in nearby foreclosures. The treatment area 

saw a 5.1% increase in foreclosures while the comparison area saw a 2.9% decrease. Both areas saw an increase in median sales 

price, but the gains were larger for the treated area (38.1% vs. 22.6% increase). Sales volume decreased in both the treatment and 

comparison areas, but the decrease was smaller in the treated area (-12.9% vs. -17.8%). 

Table 30: Changes in Treated and Comparable Areas, First Look Cook County, IL

Treated Area Comparable Area

# Census Tracts 916 916

# Foreclosures, 2013-March 2015 6,133 11,114

# Foreclosures, 2017-19 6,445 10,787

Change in Foreclosures 5.1% -2.9%

Average Median Sales Price 2013-16 $111,894 $187,573

Average Median Sales Price 2017-19 $154,510 $230,019

Change in Average Median Sales Price 38.1% 22.6%

# Sales, 2013-16 47,565 127,885

# Sales, 2017-19 41,413 105,170

Change in Sales Volume -12.9% -17.8%
© 2021 Fannie Mae Page 47 of 60

Limitations: The foreclosure data for the years 2013-15 came from the Cook County Assessor’s Office while the foreclosure data 

for 2017-19 came from ATTOM data. While both datasets were records of mortgage foreclosures, it is possible that there may be 

differences in how the data were collected that could impact the analysis. Due to the design of this intervention, targeting 

foreclosed properties available through the First Look program, wherever they were located, there were not significant clusters of 

activity. 

Emergency Bridge Loan/Soft Second Mortgage Program

Intervention Overview: Several states have established emergency home mortgage assistance programs to prevent mortgage 

foreclosures during periods of economic downturn. Emergency bridge loan programs, like Pennsylvania’s Homeowners' 

Emergency Mortgage Assistance Program (HEMAP) and Delaware’s Homeowners' Emergency Mortgage Assistance Program

(DEMAP), are intended for homeowners most likely to avoid foreclosure with help, such as those experiencing temporary income 

loss or a short-term medical crisis. The state programs provide short-term bridge loans to cover missed mortgage payments and 

prevent foreclosure. In addition to Pennsylvania and Delaware, Maryland, North Carolina, Connecticut, and Ohio have adopted 

bridge loan programs. Under these state programs, homeowners typically can apply for a short-term loan to cure past 

delinquency or to cover mortgage payments for a set period of time. Most of these programs require the homeowner to show 

some evidence that their delinquency is due to unemployment, a medical crisis,, or some other event beyond their control. The 

owner must be otherwise credit-worthy and show that the household will be able to pay their mortgage over the long-term. The 

loan is typically added onto the principal of the mortgage or added as a lien on the house that must be repaid if the house is 

refinanced or sold. Loans are paid directly to the mortgage servicer. 

Prior Studies Regarding Neighborhood Stabilization Impact: Empirical evidence reveals that many temporarily troubled 

mortgage borrowers prove able to retain their homes and pay off their mortgages in full, without a loan agreement modification, 

when offered bridge loan assistance. 

Summary of Findings: These two statewide programs were designed to assist homeowners in need, wherever they live, and 

previous studies have shown that emergency bridge loans benefit individual homeowners, near neighbors, and local government. 

However, there were not notable geographic concentrations of program activity, even in more densely populated areas of 

Pennsylvania and Delaware that had concentrations of foreclosure activity. Areas surrounding HEMAP recipients in Allegheny 

County, PA, showed some signs of strength relative to areas further from recipients—larger increase in sales volume relative to 

the comparison areas but similar declines in foreclosures and median home sales prices. Areas adjacent to DEMAP recipients did 

not show greater relative stability or improvement. The lack of association with neighborhood level stabilization may be related 

to market differences, lower density, program design (HEMAP includes an automatic “pause” on foreclosure proceedings that 

DEMAP does not), or other factors. The operation of other mortgage assistance programs including HARP and HAMP at the same 

time make it more difficult to measure impact and to identify true control areas. To achieve a neighborhood stabilization effect, 

these programs would likely need to be more geographically concentrated. But it is important to note that while there is no 

consistently discernible market impact, these programs are not without impact. To the contrary: every home that is saved from 

foreclosure has an estimated positive financial impact of more than $90,000 including local government tax revenue. In a 

previous study, Reinvestment Fund estimated that the aggregate impact of foreclosures saved by Pennsylvania’s HEMAP program 

between 2013 and 2018 was just under $300,000,000.

Note on Quick Basic Policy Analysis: The analyses presented here on these two programs do not examine causal links between 

the interventions and neighborhood stability and cannot be generalized to all local instances of an intervention type. They 

document market changes related to neighborhood stability in areas that were “treated” by a certain intervention.

Program 1: Pennsylvania Homeowners’ Emergency Mortgage Assistance Program (HEMAP)26

 Program Description: The Pennsylvania Homeowners’ Emergency Mortgage Assistance Program (HEMAP) was launched 

in 1983 to offer loans to homeowners who, through no fault of their own, were financially unable to make their mortgage 

payments and were in danger of losing their homes to foreclosure. Under Pennsylvania law, homeowners who have 

failed to pay their mortgage for at least 60 days receive an Act 91 Notice from their lender which includes information 

26Information approved as accurate by Lori Toia, Director of HEMAP on February 18, 2021.

6
© 2021 Fannie Mae Page 48 of 60

about their right to apply to the HEMAP program through PHFA-approved housing counseling agencies. While the 

application is pending, lenders/servicers cannot move forward with the foreclosure action if the applicant applies within 

33 days from the date of the Notice. PHFA has 60 days to make a determination of eligibility based on several factors 

including whether the financial hardship of the homeowner is due to circumstances beyond their control and if they have 

a reasonable prospect for resuming full payments within 36 months. If approved, HEMAP assistance can take two forms: 

continuing (i.e., an amount to reinstate the mortgage delinquency and assistance with monthly payments for 24 months, 

or 36 months in periods of high unemployment, to compensate for a loss of income) or non-continuing, a single lump 

sum to bring the mortgage current. In either instance, the current maximum loan amount is $60,000 and must be repaid. 

Loan disbursements are made directly by HEMAP to lenders on the owner's behalf. Currently HEMAP loan recipients are 

required to pay 35% of their net monthly income towards their total housing expense. Although repayment is based on 

household income, owners must pay a minimum monthly payment to HEMAP of $25.00 per month for each mortgage 

assisted. Application approval rates since the program began are at 21%. By law, mortgages insured by the Federal 

Housing Administration under Title II of the National Housing Act are not eligible under HEMAP. 

 Relevant state or local law: HEMAP was established by the General Assembly with the passage of the act of December 

23, 1983 (P. L. 385, No. 91) (Act 91), signed into law on December 12, 1983 (“Act 91”).

 Duration: 1983 to present (unfunded during some years)

 Goal: Prevent foreclosure and loss of homeownership due to circumstances beyond an owner’s control, including major 

macroeconomic shocks that result in job loss

 Agency that administers: Pennsylvania Housing Finance Agency (PHFA)

 Budget and staffing: HEMAP was initially funded through a state of Pennsylvania appropriation of $25,750,000 in 1984. 

For the majority of HEMAP’s history, the program has relied mainly on state appropriations and proceeds from 

repayment of existing loans. The State appropriations supplemented proceeds with an average of $8.5 million annually 

from 2005 to 2012 after multiple years with no appropriation. 

 Key partners: Housing counseling agencies

 Funding: HEMAP is funded by State appropriations and through repayment of existing HEMAP loans. Although the 

program has not received a state appropriation since fiscal year 2012, the Agency was entrusted with administering 

funds received by the state under the national mortgage servicer settlement agreement. A total of $60,668,000 was 

received during fiscal years 2013 through 2017. HEMAP loans over the life of the program have averaged about $11,500. 

 Key Takeaways

 HEMAP applications automatically pause a foreclosure action allowing some property owners time to obtain the 

finances needed to avoid a foreclosure even when their application for a HEMAP loan is denied.

Data Analysis 

Overview and Methods: The Fannie Mae team compared areas within a 500-foot buffer of a home that received HEMAP funds 

between 2010 and 2014 (treated area) with the rest of the census tract outside the 500-foot buffer (comparable area). The analysis 

examined the changes in the levels of mortgage foreclosure filings (2013-16 to 2017-19), median sales prices (2012-14 to 2017-19), 

and residential sales volume (2012-14 to 2017-19) in the treated and comparable areas. 

The Pennsylvania Housing Finance Authority (PHFA) provided the HEMAP loan data. Demographic and income data came from 

the U.S. Census Bureau’s American Community Survey 2015-19 5-year estimates. Sales volume, median home sales prices, 

foreclosures, and Market Value Analysis disposition came from Reinvestment Fund’s previous analyses Market Value Analysis 

(MVA) for Allegheny County. 

All census tracts are in Allegheny County but outside of the city of Pittsburgh. Census tracts are split 43% urban core and 57%

suburban. There were 330 HEMAP loan approvals across the 178 study block groups.
© 2021 Fannie Mae Page 49 of 60

Table 31: Characteristics of Treated and Comparable Areas, Homeowners’ Emergency Mortgage Assistance Program

Treated and Comparable Area

# Census Tracts 178

Average Median Household Income, 2015-19 $66,758

Housing Units per Square Mile 542

% Black 9.6%

% White 83.3%

% Hispanic 1.7%

% Asian 2.7%

Findings: Treated areas experienced a larger increase in sales volume relative to the comparable areas but similar declines in 

foreclosures and median home sales price. Areas within 500 feet of HEMAP homes saw a 57% increase in average residential sales 

volume compared with just 6% in areas outside the buffer. Foreclosure filings declined 34.9% in treated areas and 36.2% in 

comparable areas. Median sales price rose by 14.2% in treated areas and 12.1% in comparable areas. 

Table 32: Changes in Treated and Comparable Areas, Homeowners’ Emergency Mortgage Assistance Program

Treated Area Comparable Area

# Foreclosure Filings 2013-16 490 4,546

# Foreclosure Filings 2017-19 319 2,902

Change Foreclosures 2010-11, 2017-19 -34.9% -36.2%

Average Median Sales Price 2011-13 $92,897 $158,435

Average Median Sales Price 2017-19 $106,135 $177,598

Change in Median Sales Price 2012-13, 2017-19* 14.2% 12.1%

# of Residential Sales 2012-14 1,393 26,538

# of Residential Sales, 2017-19 2,193 28,249

Change in Sales 2012-14, 2017-19 57% 6%

Limitations: Previous studies of the HEMAP program have focused on the impact on homeowners; HEMAP is not designed to be 

implemented as a targeted geographic program, so there were no clusters of concentrated HEMAP loans for study. There were 

also multiple other foreclosure prevention programs operating widely at this time, both areas considered treated and 

comparison in this analysis. The exclusion of FHA-backed mortgages from this program may also limit an observable impact. 

Finally, the program itself does not necessarily prevent a foreclosure filing even if it ultimately prevents a completed foreclosure. 

Program 2: Delaware Emergency Mortgage Assistance Program (DEMAP)27

 Program description: DEMAP is a statewide program that provides emergency bridge loans to homeowners who are at 

least 90 days delinquent on their mortgage loans due to circumstances beyond their control. To be eligible, the 

27Program description information was obtained through an interview with Brian K. Rossello, Director of Housing Finance, and 

Marlena Gibson, Director of Policy and Planning at Delaware State Housing Authority, on January 14, 2021.
© 2021 Fannie Mae Page 50 of 60

homeowner’s income must be lower than 115% of the area median income, they must have lost 15% of their earning 

power, and must have a reasonable prospect of being able to make monthly mortgage payments in the near future. The 

majority of successful applicants lost their jobs, had their hours significantly reduced, or had a significant medical 

expense. The delinquent mortgage also must be on a primary residence, whether that is a one- or two-family home, 

condominium, or mobile home on a permanent foundation. The loans of up to $30,000 are paid directly to the 

lender/loan servicer in the form of a single payment to bring a mortgage current, or monthly as a continuing loan to 

cover a percentage of mortgage payments for up to 24 months. (Note: Loan maximums varied during the 15 years of the 

program, beginning at just $15,000 and rising to $50,000 during the period when HUD was offering bridge loans for two 

years under the federal Emergency Homeowners’ Loan Program (EHLP).) Loans currently come with 0% interest, but for 

at least half of the years the program has been offered since 2006 a fixed 3% simple interest rate was charged. Borrowers 

must pay 31% of their monthly income toward their mortgage payments for the duration of the loan assistance program. 

DEMAP loans may cover delinquent principal, interest, mortgage insurance, (real estate taxes, hazard insurance, and 

assessments only when escrowed in the first mortgage payment) late charges, and reasonable court costs and attorney 

fees incurred by the mortgagee. DEMAP makes no distinction around the type of loan, whether it is conventional, FHA, or 

VA. All applicants must work with a HUD approved housing counseling agency to apply. At the start of the DEMAP 

program, the state required borrowers to make monthly payments to repay the bridge loan of at least $25 or $40. The 

state has eliminated that requirement as burdensome on the borrower and see the bridge loan now as more of a 

refinancing product. Delaware does not require lenders to pause the foreclosure process until the application is 

evaluated. Acceptance rates hover around 20-25%.

 Relevant state or local law: Established as a new program under existing authority of the Delaware State Housing 

Authority. 

 Duration: 2006 to present

 Goal: To assist homeowners, who through no fault of their own, are in danger of losing their home to foreclosure

 Agency that administers: Delaware State Housing Authority’s Division of Housing Finance

 Budget: Annual allocation of $2 million. Spend approximately $750,000 in mortgage loans for newly approved borrowers 

each year. 

 Key partners: Housing counselors help applicants to apply and the Delaware Department of Justice leads a foreclosure 

mediation process between the homeowner and the lender/loan servicer.

 Funding: State budget appropriations fund the program annually. In 2013, funding was expanded for a couple of years 

by HUD Emergency Homeowners’ Loan Program (EHLP). Loan repayments are also used to fund the program.

 Key Takeaways: 

 Be agile and change the program process regularly to better achieve goals and ensure the loan will create success for 

the borrower and the program.

 Inform at-risk homeowners that help is available before late fees and attorney’s fees raise too severely the amount 

owed.

 Determine whether the goal is to get paid back. It is easier to prevent foreclosures with a quick infusion of cash if the 

program can provide a grant or forgivable loan. 

Data Analysis

Overview and Methods: Reinvestment Fund compared areas within a 500-foot buffer of homes receiving DEMAP loans between 

2010 and 2014 (treated area) with the rest of the census tract outside the 500-foot buffer (comparable area). The analysis 

examined the changes in the levels of mortgage foreclosure (2010-11 to 2017-19), median sales prices (2011-13 to 2017-19), and 

residential sales volume (2011-13 to 2017-19) in the treated and comparable areas. 

All 70 census tracts in the treated and comparable areas are in New Castle County, Delaware. Census tracts were categorized as 

“urban core” or “suburban” based on their density. Most tracts (74%) were categorized as suburban while the remaining 26%

were urban core. There were 201 DEMAP loan approvals across the treated area.
© 2021 Fannie Mae Page 51 of 60

Table 33: Characteristics of Treated and Comparable Areas, Delaware Emergency Mortgage Assistance Program

Treated and Comparable Areas

# of Census Tracts 70

Average Median Household Income, 2015-19 $63,712

Housing Units per Square Mile 472

% Black 31.7%

% White 48.7%

% Hispanic 12.1%

% Asian 4.9%

Findings: The treated areas saw a decrease in median sales price while comparable areas saw an increase. Residential sales 

volume decreased for both the treated and comparable areas but decreased more in the treated areas. Foreclosures decreased in

both the treated and comparable areas but declined more in the comparable areas. Average median sales price for treated areas 

decreased 6.2% while the average median sales price increased by 41.7% in comparable areas. Residential sales volume 

decreased by 3.9% in treated areas and decreased by 42.8% in comparable areas. The decrease was more substantial in 

comparable areas (-72.8% vs -11.4%). 

Table 34: Changes in Treated and Comparable Areas, Delaware Emergency Mortgage Assistance Program

Treated Area Comparable Area

Foreclosures 2011-13 395 6,635

Foreclosures 2017-19 350 1,804

Change in Annual Foreclosures 2010-11, 2017-19 -11.4% -72.8%

Average Median Sales Price 2011-13 $117,840 $173,581

Average Median Sales Price 2017-19 $110,506 $246,015

Change in Average Median Sales Price 2011-13, 2017-19 -6.2% 41.7%

# of Sales 2011-13 1,158 7,388

# of sales 2017-19 1,113 4,226

Change in Average # of Yearly Sales 2011-13, 2017-19 -3.9% -42.8%

Limitations: Source data for sales and foreclosures were different in the two time periods. It is possible that there may be 

differences in how the data were collected that could impact the analysis. Additionally, similar factors apply as in the 

Pennsylvania mortgage assistance program—such as the contemporaneous operation of multiple foreclosure prevention 

programs that may have included properties in both treated and comparison areas. 
© 2021 Fannie Mae Page 52 of 60

Conclusion

Neighborhoods are negatively impacted by macroeconomic shocks 

beyond their control. The economic impacts are particularly acute 

for neighborhoods of color that have been weakened by historically 

racist policies like redlining, race-based zoning ordinances, and 

restrictive covenants. 

Changing the trajectory of a neighborhood is extremely difficult. 

Government leaders and practitioners tend to look to a defined set of 

standard tools to attempt to stabilize or improve neighborhoods 

wrestling with the negative impacts of an economic shock. In many 

cases, the economic crisis exacerbates existing challenges presented 

by long-term disinvestment and declining population, as well as 

deteriorated housing stock and infrastructure. 

The new research provided in this report does not provide definitive 

answers, but it does indicate the promise of several interventions 

that localities and their partners can implement to help residential 

neighborhoods withstand a national or global economic crisis. The 

analyses as a whole offer five important lessons.

First, data collection and evaluation are essential to enlarging our 

knowledge about what works effectively to stabilize low-income and 

middle neighborhoods. Several promising programs did not collect 

data that would allow for even a basic evaluation. Others kept data 

in antiquated databases that could not report out that data in a form 

that was useful, and a few even discarded data once reporting 

requirements with funders or regulatory agencies were met. Few, if 

any, had dedicated funding for data collection or analysis. 

Second, scale is important. Several of the programs identified were 

innovative pilot programs but were tested at such a limited level, or 

were spread across large geographies rather than concentrated, that 

the program did not show an impact. Municipalities that invested in programs with sufficient levels of activity within specific 

geographies for a significant number of years were more likely to influence a neighborhood.

Third, targeting limited resources is essential to impacting a neighborhood. Many programs seek to approach a problem at a 

citywide or even statewide level, providing help to affected individuals or properties, wherever they are located. Providing limited 

and dispersed help to a vast area renders it far less likely to change a neighborhood’s trajectory, even if individual program 

participants benefit materially. For example, many First Look efforts were somewhat diffuse across counties or entire metro 

areas. That said, it should also be noted that there remains value in programs that target positive outcomes for individual 

households or homes. Every homeowner that retains their home and every distressed property that is restored to active use is a 

positive outcome that should not be discounted. However, the benefits of such programs may not extend to neighborhood level 

impact. Therefore, policy makers and practitioners should be clear with regard to their intentions for program outcomes, and 

whether the primary focus is on individual homes and households or on broader neighborhoods and communities. 

Fourth, the specifics of program design and implementation significantly affect outcomes. Even where one of two programs 

analyzed was modeled on the first, differences in design and implementation may cause differences in impact. For example, 

DEMAP, an emergency bridge loan program, was modeled on the Pennsylvania program HEMAP, but DEMAP had several 

differences in program design including the fact they DEMAP did not pause foreclosure proceedings for applicants during the time

period in which their application was evaluated. These differences may have impacted the program’s observable outcomes.

Recognition of how program features impact outcomes motivated the creation of short case studies detailing the program 

design, level of investment, and implementation approach for each program to allow practitioners to understand the key 

attributes that contributed to the outcomes analyzed.

Providing limited and 

dispersed help to a vast area 

renders it far less likely to 

change a neighborhood’s 

trajectory, even if individual 

program participants 

benefit materially.
© 2021 Fannie Mae Page 53 of 60

Fifth, no metric for analyzing whether a specific program positively impacted a neighborhood is definitive. Often, programs were 

designed without a specific goal or were developed in hopes that they would achieve multiple goals. Although change in home 

sales price is a commonly used indicator of neighborhood market stability, and it was the most universally available propertylevel metric, it must be properly evaluated in context. For neighborhoods with low property values, an increase in sales prices can 

be critical to welcoming new investment and protecting the home equity of residents who may have limited wealth apart from 

their investment in their home. It can also point to displacement and gentrification. Within this exercise, the team endeavored to 

understand whether increases in sales price indicated stability for low- or middle-income residents, or a demographic 

transformation reflecting the replacement of one group of residents with another, higher-income group. Though many of the 

neighborhoods evaluated for this effort saw growth in sales price during the study period, they did not see the sort of dramatic 

demographic and home price shifts typically associated with gentrification.

This report seeks to contribute to the use of evidence-based policies to stabilize communities vulnerable to national and global 

economic shocks. While every community is unique in its context, challenges, and opportunities, we hope that local practitioners 

find useful the information contained in this report and that it informs their efforts to address the specific needs of their 

neighborhoods. Fannie Mae stands ready to partner with local governments and practitioners in the pursuit of strategies that 

contribute to stability and recovery, and that promote equity and opportunity. 
© 2021 Fannie Mae Page 54 of 60

APPENDIX A: Indicators of Potential Displacement (Gentrification)

The following tables show changes in the population of different racial and ethnic groups as well as in median incomes in treated 

and comparison areas for each of the interventions studied in this report. In each table, the sum is the total count of all block 

group or tract populations by race and ethnicity, while the average is the average count per block group or tract in that category. 

The percent change is the average of all block or tract-level population changes, rather than the pooled average change across 

the block groups or tracts. These tables were not included in the core analytic work conducted for this report but are provided as 

information to readers who wish to know more about the nature of change in these areas. In general, these tables show that 

treated areas did not appear to experience major increases in income or declines in non-white populations overall or when 

contrasted with trends in comparable areas. 

Note that calculating an average of a group of changes can result in seemingly divergent trends: in two cases, both in Kansas City, 

the average percent change moved in the opposite direction of the average block group population due to small block group level 

populations. These are different ways of evaluating change across a set of block groups. First Look and Emergency Bridge Loans, 

comparisons were made inside and outside a buffered area within a single tract, so only change over time by tract can be 

compared. 

Code Enforcement

Baltimore, MD

Middle Market Stressed

Treated Area

% Change

Comparable Area

% Change

Start Year End Year Start Year End Year

Sum Average Sum Average Average Sum Average Sum Average Average

Black 

Population 58,980 737 55,084 688 -7.4% 16,657 574 14,123 487 -14.4%

White 

Population 9,135 114 9,586 119 37.6% 8,766 302 8,427 290 18.1%

Hispanic 

Population 3,240 40 3,856 48 105.2% 2,416 83 2,654 91 57.7%

Asian 

Population 756 9 987 12 62.9% 592 20 484 16 153.1%

Med. HH 

Income $37,198 $46,733 $9,535 $34,803 $42,633 $7,830

Metro Median 

Income $66,195 $81,598 $15,403 $66,195 $81,598 $15,403
© 2021 Fannie Mae Page 55 of 60

Middle Market

Treated Area

% Change

Comparable Area

% Change

Start Year End Year Start Year End Year

Sum Average Sum Average Average Sum Average Sum Average Average

Black Population 63,615 795 64,959 811 4.5% 8,174 743 9,266 842 8.3%

White Population 15,918 198 15,878 198 17.9% 1,247 113 951 86 -2.0%

Hispanic Population 4,724 59 5,931 74 53.1% 179 16 389 35 96.9%

Asian Population 1,165 14 1,016 12 11.4% 45 4 58 5 165.6%

Med. HH Income $44,502 $52,937 $8,435 $34,089 $41,037 $6,948

Metro Median 

Income $66,195 $81,598 $15,403 $66,195 $81,598 $15,403

Middle Market Choice

Treated Area

% Change

Comparable Area

% Change

Start Year End Year Start Year End Year

Sum Average Sum Sverage Average Sum Average Sum Average Average

Black Population 46,238 543 48,344 568 10.8% 18,879 651 18,668 643 -9.0%

White Population 32,149 378 29,289 344 0.8% 8,965 309 9,115 314 9.2%

Hispanic Population 3,789 44 3,561 41 23.9% 1,175 40 919 31 63.3%

Asian Population 1,362 16 1,354 15 19.5% 560 19 774 26 53.2%

Med. HH Income $54,002 $70,655 $16,653 $49,262 $67,631 $18,369

Metro Median 

Income $66,195 $81,598 $15,403 $66,195 $81,598 $15,403

Minneapolis, MN 

Treated Area 

% Change

Comparable Area 

% Change

Start Year End Year Start Year End Year

Sum Average Sum Average Average Sum Average Sum Average Average

Black Population 31,590 244 35,793 277 23.0% 21,026 173 23,301 192 15.9%

White Population 57,349 444 61,346 475 11.1% 74,191 613 82,582 682 10.6%

Hispanic Population 17,186 133 17,698 137 23.0% 16,759 138 15,529 128 -1.7%

Asian Population 8,668 67 9,464 73 22.4% 5,426 44 5,704 47 12.7%

Med. HH Income $75,384 $86,940 $11,556 $84,172 $105,290 $21,118

Metro Median 

Income $65,181 $80,421 $15,240 $65,181 $80,421 $15,240
© 2021 Fannie Mae Page 56 of 60

Demolition

Kansas City, MO (2010-14 to 2015-19)

Treated Area Treated Area 

% 

Change

Comparable Area 

Start Year

Comparable Area 

End Year

% 

Start Year End Year Start Year End Year Change

Sum Average Sum Average Sum Average Sum average

Black Households 5,219 474 4,310 392 12% 10,904 496 11,807 537 14%

White Households 1,157 105 1,296 118 -17% 1,541 70 2,291 104 167%

Hispanic 

Households 2,927 266 4,039 367 38% 2,459 112 3,561 162 169%

Asian Households 98 9 7 1 -93% 466 21 237 11 40%

Med. HH Income $23,953 $31,434 31% $25,382 $31,934 40%

Metro Median 

Income $57,056 $66,632 $57,056 $66,632

Hennepin County, MN (2008-12 to 2015-19)

Treated Area Treated Area 

% 

Change

Comparable Area 

Start Year

Comparable Area 

End Year % 

Start Year End Year Start Year End Year Change

Sum Average Sum Average Sum Average Sum Average

Black 

Households 6,066 758 7,781 973 32% 1,257 201 617 76 6%

White 

Households 3,212 402 3,406 426 9% 2,679 387 2,708 489 18%

Hispanic 

Households 1,974 247 2,911 364 90% 1,033 102 1,281 183 23%

Asian 

Households 2,129 266 2,870 359 52% 94 6 282 35 535%

Med. HH 

Income $33,129 $45,612 41% $35,940 $70,247 157%

Metro Median 

Income $66,751 $80,421 $66,751 $80,421
© 2021 Fannie Mae Page 57 of 60

Philadelphia, PA (2010-14 to 2015-19)

Treated Area Treated Area 

% 

Change

Comparable Area 

Start Year

Comparable Area 

End Year % 

Start Year End Year Start Year End Year Change

Sum Average Sum Average Sum Average Sum Average

Black 

Households 5,389 898 5,570 928 3% 10,659 592 13,098 728 34%

White 

Households 311 52 427 71 37% 1,307 73 956 53 507%

Hispanic 

Households 25 4 139 23 456% 2,587 144 3,386 188 33%

Asian 

Households 6 1 160 27 2,567% 488 27 324 18 -73%

Med. HH 

Income $18,547 $21,403 15% $24,484 $39,028 72%

Metro Median 

Income $62,169 $72,343 $62,169 $72,343

Scattered Site Rehabilitation

DeKalb County, GA (2007-11 to 2015-19)

Treated Area Treated Area

% Change

Comparable Area 

Start Year

Comparable Area 

End Year % 

Start Year End Year Start Year End Year Change

Sum Average Sum Average Sum Average Sum Average

Black 

Households 14,223 3,556 12,396 3,099 -11% 27,804 2,317 34,657 2,888 28%

White 

Households 356 89 607 152 90% 875 73 775 65 56%

Hispanic 

Households 325 81 405 101 39% 259 22 629 52 176%

Asian 

Households 72 18 22 6 -100% 227 19 276 23 -27%

Med. HH 

Income $62,445 $71,700 15% $56,787 $65,543 17%

Metro Median 

Income $57,783 $68,316 $57,783 $68,316
© 2021 Fannie Mae Page 58 of 60

Land Banks

Albany, NY

Treated Area % 

Change

Comparable Area % 

Change Start Year End Year Start Year End Year

Sum Average Sum Average Average Sum Average Sum Average Average

Black 

Population 16,212 540 13,372 445 -19.2% 3,016 232 2,948 226 2.2%

White 

Population 9,999 333 8,119 270 -23.5% 8,789 676 7,899 607 -11.6%

Hispanic 

Population 4,047 134 4,800 160 14.5% 1,074 82 1,488 114 35.8%

Asian 

Population 1,178 39 1,883 62 17.3% 734 56 1,284 98 33.3%

Med. HH 

Income $31,294 $32,200 $906 $44,277 $50,800 $6,523

Metro Median 

Income $57,781 $71,285 $13,504 $57,781 $71,285 $13,504

Houston, TX

F Market

Treated Area

% Change

Comparable Area

% Change

Start Year End Year Start Year End Year

Sum Average Sum Average Average Sum Average Sum Average Average

Black Population 4,437 833 3,515 669 -16.4% 3,882 971 4,554 1,139 27.3%

White Population 55 16 81 24 221.7% 94 24 16 4 -90.0%

Hispanic Population 1,341 359 1,756 585 86.7% 689 172 1,137 284 73.1%

Asian Population 19 3 0 0 -100.0% 21 5 56 14 115.4%

Med. HH Income $19,014 $32,928 $13,914 $34,828 $32,561 -$2,267

Metro Median Income $55,207 $67,516 $12,309 $55,207 $67,516 $12,309

G Market

Treated Area

% Change

Comparable Area

% Change

Start Year End Year Start Year End Year

Sum Average Sum Average Average Sum Average Sum Average Average

Black Population 3,078 1,539 2,254 1,127 -26.7% 2,269 756 1,434 478 -36.1%

White Population 41 21 36 18 -15.7% 63 21 102 34 175.0%

Hispanic Population 614 307 1,003 502 71.8% 819 273 976 325 48.1%
© 2021 Fannie Mae Page 59 of 60

Asian Population 24 12 0 0 -100.0% 10 3 8 3 -33.3%

Med. HH Income $22,552 $37,969 $15,417 $22,148 $16,817 -$5,331

Metro Median Income $55,207 $67,516 $12,309 $55,207 $67,516 $12,309

H Market

Treated Area

% Change

Comparable Area

% Change

Start Year End Year Start Year End Year

Sum Average Sum Average Average Sum Average Sum Average Average

Black Population 4,666 778 4,171 695 -5.7% 11,934 918 10,679 821 -12.9%

White Population 118 20 152 25 52.8% 197 15 416 32 186.9%

Hispanic Population 1,773 296 2,032 339 85.5% 3,762 289 5,846 450 135.2%

Asian Population 4 1 31 5 350.0% 20 2 21 2 -12.2%

Med. HH Income $22,767 $39,057 $16,290 $28,842 $34,035 $5,193

Metro Median Income $55,207 $67,516 $12,309 $55,207 $67,516 $12,309

First Look Programs

Essex County, NJ 

Treated Area Treated Area

Start Year End Year % Change

Sum Average Sum Average

Black Population 254,311 1,856 251,955 1,839 -0.9%

White Population 86,163 628 85,051 620 -1.3%

Hispanic Population 122,970 897 130,555 952 6.1%

Asian Population 16,899 123 16,888 123 0.0%

Med. HH Income $48,478 $54,745 12.9%

Metro Median Income $66,902 $78,773 17.7%
© 2021 Fannie Mae Page 60 of 60

Cook County, IL 

Emergency Bridge Loan/Soft Second Mortgage Programs

Pennsylvania/ Analysis of Allegheny County 

Treated Area Treated Area

Start Year End Year % Change

Sum Average Sum Average

Black Population 62,368 350 64,458 362 3.4%

White Population 576,596 3,239 561,112 3,152 -2.7%

Hispanic Population 9,455 53 11,397 64 20.8%

Asian Population 15,582 87 17,732 99 13.8%

Med. HH Income $57,620 $66,758 15.9%

Metro Median Income $51,883 $60,535 16.7%

Delaware/ Analysis of Wilmington

Treated Area Treated Area 

Start Year End Year % Change

Sum Average Sum Average

Black Population 100,325 1,433 104,231 1,489 3.9%

White Population 165,353 2,362 160,214 2,288 -3.1%

Hispanic Population 35,266 503 39,836 569 13.1%

Asian Population 12,172 173 5,904 227 31.2%

Med. HH Income $57,000 $63,712 11.8%

Metro Median Income $62,169 $72,343 16.4%

Treated Area Treated Area

Start Year End Year % Change

Sum Average Sum Average

Black Population 1,129,726 1,233 1,083,957 1,183 -4.1%

White Population 1,387,414 1,514 1,306,040 1,425 -5.9%

Hispanic Population 1,038,667 1,133 1,080,094 1,179 4.1%

Asian Population 198,895 217 210,514 229 5.5%

Med. HH Income $50,098 $58,443 16.7%

Metro Median Income $ 61,497 $71,770 16.7%