Estimating Home Equity Impacts from Rapid,
Targeted Residential Demolition in Detroit, MI:
Application of a Spatially-Dynamic Data System for
Decision Support
A Report Produced by Dynamo Metrics, LLC1
July 2015
ABSTRACT
In an effort to further the goals of the Motor City Mapping Initiative, investments were made to
create a data architecture for a spatially-dynamic decision support system in Detroit, Michigan.
The data system that now exists is capable of tracking the time series dynamics of every one of
the more than 384,000 parcels in Detroit between January 1st, 2011 and March 31st, 2015 on a
quarterly basis (seventeen quarterly time steps for each parcel). To provide a rapidly produced
and useful example of the analytic capabilities of the spatially-dynamic data system, it was used
to estimate the effect of the in progress, rapidly deployed Hardest Hit Fund (HHF) demolition
investment concentrated in selected areas of Detroit between April 1st, 2014 (Q2 2014) and
March 31st, 2015 (Q1 2015). This study utilizes causal modeling that incorporates spatiallydynamic econometric methods in the context of a spatio-temporal treatment effects analysis to
estimate the impact of the HHF Blight Elimination Program implementation on single-family
home values in Detroit.
Using the year prior to HHF implementation (Q2 2013 – Q1 2014) as a control for the rapid and
targeted HHF implementation (Q2 2014-Q1 2015), findings suggest that home equity increases
of up to 13.8%2 exist for single-family homes that sold inside HHF demolition zones (HHF Zones)
after the implementation of the HHF Blight Elimination Program. Further results suggest each
demolition event within HHF Zones after policy implementation nets a 4.2% positive impact on
the value of nearby homes, while single-family home counterparts outside the HHF Zones net a
2.1% positive impact on the value of nearby homes during the same time period from nearby
demolition activity. Findings thus suggest that home equity protection hedges created by
demolition activity are greater, and homes are more valuable overall, within HHF Zones after
HHF implementation than elsewhere in the city. It can therefore reasonably be maintained that
the HHF Blight Elimination Program is having a market-stabilizing effect on the neighborhoods it
targets.
1
Report authors can be reached at info@dynamometrics.com or www.dynamometrics.com.
The “up to” 13.8% suggests that more research is warranted to determine the portion of the 13.8% that is specifically caused by
HHF demolition activity, and what portion, if any, can be attributed to the many other positive neighborhood activities, investments
and associated effects that may have stemmed from targeted HHF investments.
2
1
Acknowledgements
The team at Dynamo Metrics would like to thank Rock Ventures, LLC, and The Skillman
Foundation for helping make this project possible and for their dedication to the increased
sophistication of data systems in Detroit. Also, thanks to all partners of the Motor City Mapping
initiative and those departments in the city of Detroit that provided data for this study for their
commitment to a rich data environment in Detroit. Thanks to Data Driven Detroit and
LOVELAND Technologies, LLC for project support and help integrating into the Detroit data
movement.
2
Table of Contents
Acknowledgements ....................................................................................................2
Introduction ...............................................................................................................4
Increasing the Sophistication of Detroit’s Data Ecosystem............................................................. 4
Constructing a Spatially-Dynamic Data System in Detroit .............................................................. 4
Identifying Useful Application of a Spatially-Dynamic Data System in Detroit ............................... 4
Estimating Home Equity Impacts from Rapid, Targeted Residential Demolition in
Detroit, MI ..................................................................................................................6
Empirical Research Approach .......................................................................................................... 6
Data ................................................................................................................................................. 6
Empirical Model............................................................................................................................... 7
Empirical Findings ............................................................................................................................ 9
Interpretation of Key Empirical Findings ................................................................................... 11
Policy Implications .................................................................................................... 15
Study Limitations and Future Research...................................................................... 16
References ................................................................................................................ 17
Appendices ............................................................................................................... 19
1. Cautionary Note on Summary Statistics .................................................................................... 20
2. Summary Statistics of Average Single-family Homes: Inside/Outside HHF Zones & Before/After
HHF Implementation ..................................................................................................................... 21
3. Summary Stats of Average Single-family Homes: Housing Submarkets Based on Block-Group
Level Median Income .................................................................................................................... 22
4. Summary Statistics of Average Single-family Homes: Median Income Housing Submarkets
Inside/Outside HHF Zones ............................................................................................................. 23
5. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in High
Income Submarket ........................................................................................................................ 24
6. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in Middle-High
Income Submarket ........................................................................................................................ 25
7. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in Middle-Low
Income Submarket ........................................................................................................................ 26
8. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in Low Income
Submarket ..................................................................................................................................... 27
9. Map of Submarkets for this Analysis: 5-Year Census Estimates of Median Income at Block
Group Level ................................................................................................................................... 28
10. Map of Demolition Concentrations Before HHF Implementation (Q2 2013-Q1 2014) .......... 29
11. Map of Demolition Concentrations After HHF Implementation (Q2 2014-Q1 2015) ............. 30
12. Map of Average Single-Family Sale Price Concentrations Before HHF (Q2 2013-Q1 2014) ... 31
13. Map of Average Single-Family Sale Price Concentrations After HHF (Q2 2014 – Q1 2015) ... 32
14. Map of Unoccupied Tax Foreclosable Residential Properties in 2015 Q1 .............................. 33
15. Map of Vacant Lot Concentrations in 2015 Q1 ....................................................................... 34
16. Map of Average Sales Price Change After HHF Blight Elimination Program Implementation 35
17. Chow Test Results - HHF Regimes Diagnostics ........................................................................ 36
3
Introduction
Increasing the Sophistication of Detroit’s Data Ecosystem
To further the goals of the Motor City Mapping initiative, The Skillman Foundation and Rock
Ventures, LLC, sought to develop data analytics capabilities for Detroit. Dynamo Metrics, LLC
(Dynamo), was hired for two tasks. First, Dynamo was to construct a spatially-dynamic and
analytics ready data system from available public and proprietary information. Second, Dynamo
was to produce an analytics product derived from this data system to show the usefulness of a
sustained commitment to data infrastructure.
From December 2014 through May 2015, Dynamo constructed the spatially-dynamic data
system. During this time, it was decided that the Detroit policy community may benefit from an
analytics product focused on the impact of the HHF Blight Elimination Program investments now
underway. Dynamo explored both correlative and causal modeling approaches. In June 2015
Dynamo determined that it could construct a causal modeling approach, although the
explanatory power of the causal model would be less robust than in normally-functioning real
estate markets. Time constraints limited Dynamo’s ability to exhaustively test and stress the
modeling process, as would be expected in an academic environment. Those limitations
notwithstanding, this report’s results provide insight, performance measurement, and decision
support to Detroit’s leadership, and show examples of what can be quickly produced if Detroit
makes a sustained commitment to data infrastructure.
Constructing a Spatially-Dynamic Data System in Detroit
A time series oriented data system that tracks change in property-level and neighborhood-level
characteristics is critical to performing analysis on the dynamics of property values, home
occupancy, foreclosure, crime, blight, vacant lots, and other localized neighborhood health
phenomena. Understanding these dynamics creates the ability to test performance and quantify
return on investment (ROI) from various policies and programs. To this end, Dynamo created a
data system in Detroit (Detroit Space-Time Analytics Data System™, or D-STADS™) that tracks
property characteristics and their quarterly dynamics at the parcel level. D-STADS™ currently
tracks from first-quarter of 2011 through the first-quarter of 2015. D-STADS™ allows for
customizable property intervention analytics enabling quantification of positive and negative
externalities associated with events such as nearby properties being sold, blighted properties
being demolished, nearby properties being renovated, and beyond. The inherently spatial
architecture of D-STADS™ further allows for the production of dynamic illustrations of unfolding
events in static and online map-based visualization formats.
Identifying Useful Application of a Spatially-Dynamic Data System in Detroit
In 2010 U.S. Treasury’ Hardest Hit Fund (HHF) made $7.6 billion available for foreclosure
prevention in 18 states, including Michigan. Michigan’s share was over $498 million. In 2013
Michigan was allowed to reprogram $100 million of this HHF money for demolition of blighted
residential structures. Detroit was allocated $52.3 million of the $100 million. Since then
Michigan has allocated an additional $75 million of HHF money to blight elimination, with $50
million of it committed to Detroit. U.S. Treasury allowed Detroit to spend HHF money on blight
4
elimination because previous research3 demonstrated through causal modeling that blight
demolition preserves surrounding property values. That research also established strong
correlation between blight elimination and reduction in mortgage foreclosure rates.
This study builds upon that previous research. This study further tests the theory that
demolition of blighted residential structures in targeted neighborhoods creates positive
property value impacts for nearby houses in those neighborhoods. The primary difference
between previous blight elimination research and this research application is that demolition
occurred in targeted zones4 and that demolition was rapidly performed. This circumstance and
an operationalized D-STADS™ allow for concise space-time testing of the HHF Blight Elimination
policy in terms of how rapid, concentrated demolition impacts residential property value. Such
testing may reasonably lead to consideration of further targeted and rapid blight elimination
investment in Detroit and similar communities nationwide.
3
See: Griswold, N., Calnin. B., Schramm, M., Anselin, L. and P. Boehnlein. 2014. Estimating the Effect of Demolishing Distressed
Structures in Cleveland, OH, 2009-2013: Impacts on Real Estate Equity and Mortgage-Foreclosure. Publication of the Western
Reserve Land Conservancy: Thriving Communities Institute.
4 Ibid.
5
Estimating Home Equity Impacts from Rapid, Targeted
Residential Demolition in Detroit, MI
Empirical Research Approach
The delineation of the HHF Zones and the implementation of the HHF Blight Elimination
Program in Detroit can be viewed as “natural experiments” or “treatments” in a quasiexperimental sense (for methodological background, see, e.g., the recent reviews in Angrist and
Pischke, 2015, and Imbens and Rubin, 2015). Policymakers are interested in the effect of the
rapid and targeted demolition experiment on specific outcome variables, such as residential
house sales prices. A typical analysis of these effects is regression discontinuity (a change in the
time trend for an outcome variable after the treatment is applied) or difference-in-differences
(comparing the before-and-after difference in outcome between treated and untreated
observations). The HHF Blight Elimination Program is more challenging, however, because the
sales in question pertain to different properties, so there are no observations (sales) before the
experiment (demolition) for a property that sold after the experiment. As a result, a traditional
regression discontinuity or difference-in-differences analysis cannot be carried out. In this
study’s regression analysis, this challenge is addressed by separating the sales observations into
different “regimes” (within HHF Zones and outside of HHF Zones), and only then comparing the
value attributed to key policy variables in a hedonic regression before and after the HHF
implementation (the second quarter of 2014) (for details on spatial regimes regression, see
Anselin and Rey, 2014). In other words, this study measures how selling inside/outside HHF
Zones, and before/after HHF Blight Elimination Program implementation, impact house sale
prices.
Given that targeted and rapid demolition went into full motion in April of 2014, the best data
that D-STADS™ has during the policy covers one year (four quarterly time-steps): Q2 2014 - Q1
2015. A proper control group was constructed by extending the observation period back in time
to Q2 2013 to create a two-year total time series for the study (Q2 2013 - Q1 2015). April 1st,
2014 is the before and after break of single-family sales observations given it is the estimated
start-date of the HHF demolition program.
Data
An intensive process of data quality control and assessment was undertaken to make sure the
data was consistent, as error free as possible5, and linked across each data source in a common
format to achieve readiness for causal and other statistical modeling. Property status in terms of
sales, occupancy, crime, and all other data used for analytics were identified for every property
in every quarterly time-step in Detroit. Data from the following sources was incorporated:



5
Detroit City Assessor (Property characteristics; Property ownership transfers)
Wayne County Treasurer (Tax foreclosure eligible properties)
Valassis Corporation (Postal vacancy)
Please contact the authors for explanation of specific data processing details and issues within the Detroit data system.
6




City of Detroit Police Department (Crime)
Detroit Open Data Portal (Parcel shapefile)
Data Driven Detroit (HHF Zone boundaries)
US Census Bureau (Tigerline Block Group shapefile and income data)
In addition, each property received a summary of the characteristics and conditions of all
properties within 500 feet of it during each quarterly time step. With the dynamic change of the
immediate environment surrounding each individual property accounted for over time, analysis
which tracks the causalities and key trends of change can be studied. The result of this critical
step in data processing is the Detroit Space-Time Analytics Data System™.
This study used D-STADS™ for the limited purpose of analyzing the impact of HHF demolitions
carried out through the first quarter of 2015 as an exercise in its analytic capacity. If D-STADS™
is kept current and robust it can become a powerful tool for decision makers, researchers and
practitioners alike. The future use of D-STADS™ is envisioned as the heart of an online userfriendly decision support tool with an online map-based visualization capability and interface
that allows users to analyze and simulate customizable property interventions and understand
their impact.
Empirical Model
Empirical methods associated with the hedonic price function are based on the economic theory
that goods are ultimately valued by way of their utility-bearing attributes (Lancaster, 1966;
Rosen, 1974). While specific application of these methods may vary, contemporary literature
provides strong evidence of the negative spillover effects on real estate values from nearby
distressed properties (Simons, Quercia and Maric, 1998; Immergluck and Smith, 2006; Griswold,
2006; Griswold and Norris, 2007; Schuetz, et al., 2008; Mikelbank, 2008; Leonard and Murdoch,
2009; Harding, et al., 2009; Rogers and Winter, 2009; Lin, Rosenblatt and Yao, 2009; Kobie 2003;
Rogers, 2010; Hartley, 2010; Campbell et al., 2011; Groves and Rogers, 2011; Whitaker and
Fitzpatrick, 2013; Griswold and Calnin, et al. , 2014). The empirical models used in this study
leverage D-STADS™ to build upon established model design and evidence. Here, the model is
specifically designed to test the impact of distressed housing and vacant lots on housing values
in the context of the implementation of the HHF Blight Elimination Program as a “natural
experiment” or “treatment” in a quasi-experimental environment (Angrist and Pischke, 2015,
and Imbens and Rubin, 2015).
The specific empirical model design for this research application is as follows:
ln(𝑃𝑖 ) = 𝛽0 + 𝛽1 (𝐷𝑖𝑅 ) + 𝛽2 (𝑃𝑖𝐻𝐻𝐹 ) + 𝛽3 (𝑆𝑖 ) + 𝛽4 (𝑆𝑀𝑖 ) + 𝛽5 (𝑆𝑉𝑖 ) + 𝛽6 (𝑆𝑇𝑖 ) + 𝑢𝑖
Where Pi , the natural log of Detroit’s single-family home housing prices between Q2 2013 – Q1
2015 - is determined by:
7
1. Several vectors from several spatial variables, 𝛽1 (𝐷𝑖𝑅 ), measuring the aggregate count
of multiple types of residential structures and vacant lots within 500 feet of a residential
sale during the quarter in which it sold6;
2. Several vectors from several HHF policy variables, 𝛽2 (𝑃𝑖𝐻𝐻𝐹 ), estimating the effect of
before (Q2 2013-Q1 2014) and after (Q2 2014-Q1 2015) HHF implementation, as well as
the effect of being inside and outside the HHF impact zones;
3. Several vectors of variables describing the physical attributes of sold houses, 𝛽3 (𝑆𝑖 );
4. Several vectors of indicator variables, 𝛽4 (𝑆𝑀𝑖 ), identifying the Detroit income-based
housing submarket a house sold in. Submarkets were identified at the block group level
using a 5-year U.S. Census Bureau estimate of Median Household Income;
5. Several vectors of indicator variables, 𝛽5 (𝑆𝑉𝑖 ), identifying the quarter a home sold in an
attempt to control for the seasonal effects of homes sales;
6. Given Detroit’s distressed market, several vectors of indicator variables, 𝛽6 (𝑆𝑇𝑖 ),
account for the sale/deed type of the house that sold, and;
7. The error term, ui , in all analyses accounts for the presence of heteroskedasticity of
unknown form.
Three versions of the empirical model were run in this analysis to provide necessary insight into
the effect of distressed structures, vacant lots and rapid, targeted HHF demolition intervention.
First, a citywide version of the model was run which incorporates indicator variables identifying:
1) whether a single-family home sold inside or outside the targeted HHF Zones; and 2) whether
a single-family home sold before or after the implementation of the HHF Blight Elimination
Program. Second, a separate regression was run for only those sales that occurred within an
HHF designated zone. Third, a separate regression was run for the sales occurring outside the
designated HHF Zones. These two separate regressions can be thought of as “spatial regimes”
and taken together in a “regimes regression” they allow an assessment of the extent to which
the model coefficients differ between regimes, hence an assessment of the difference between
the housing markets inside and outside HHF Zones. That said, they are presented separately to
allow the contrast between sales behavior inside and outside HHF Zones. The regimes model
requires that the HHF “inside/outside” variable be dropped given that it was the indicator
variable used to split the citywide observations into the two respective regimes. Each model –
citywide and regimes - possess distressed structure and vacant lot counts, as well as associated
before/after HHF interaction variables that quantify the home value effect of turning blighted
structures into vacant lots by way of nearby demolition events (500 feet in all model versions).
6
A unique space-time lag variable was designed for this analysis to capture highly localized changes in the housing market and
neighborhood effects as they vary over time (see Anselin, 1988 and Anselin and Rey 2014 for technical details on the concept of a
spatially lagged variable). It is constructed in the spirit of the “comparable sales” approach in real estate appraisal. For each property
sold in a given quarter, the average is computed of the sales prices of the sixth closest (nearest neighbor) properties that sold in the
quarter before. In our analysis, we used a logarithmic transformation for the sales price variable and constructed the space-time
lagged variable from those logs. Hence its coefficient can be interpreted as the percentage change in sales price resulting from a
percentage change in the “comparable” sales in the previous period. Since this variable takes on a different value for each sale, and
varies both across space and over time, it is deemed to be more adept at reflecting variations in local market (and by implication,
neighborhood) conditions than a standard spatial fixed effect indicator variable. A spatial fixed effect variable would take on the
value of 1 for all sales at all periods that happen in the same spatial area, typically a census tract or block group, and thus would not
capture any changes over time (see Anselin and Arribas-Bel 2013, for a technical discussion).
8
Empirical Findings
Table 1 below provides results from the three versions of empirical model specified above. The
total number of single-family home sales identified during the study time-period is 8,386, with
5,526 occurring inside the HHF Zone regime and 2,860 occurring outside the HHF Zone regime.
The dependent variable in the regressions is based on the actual sales price of the 8,386 singlefamily home observations in Detroit over the study time period. Variables identified as
explanatory in the regression of single-family house sales price are divided into six groupings: 1)
the spatial variables provide counts of particular types of properties surrounding the
observation of interest; 2) the policy variables are designed as indicator variables of whether a
sales observation occurred inside/outside or before/after implementation of HHF as well as the
interaction effect of some spatial variables after HHF implementation; 3) the physical variables
explain the attributes of the single-family home that sold ; 4) the submarket variables are tiered
into four levels of median income at the block group level; 5) the seasonal variables indicate
which season a property sold in; and, 6) the sales type variables indicate which type of deed
transfer occurred in the sale of the property. The regression analysis delivers “coefficients”
associated with each variable we hypothesize to impact home sales price. These coefficient
estimates portray the percentage effect that a given variable has on home sales price.
Specifically, coefficients are read as percent impact on sales price from a marginal change in the
variable – i.e. if bathrooms = “0.086*”, then one additional bathroom increases sales price by
8.6%. A coefficient can only be interpreted this way if the coefficient is statistically significant –
i.e. “*”, “**” or “***” is placed next to the coefficient.
The following are some key summary statistics7 that provide a sense of the magnitude8 of the
HHF Blight Elimination Program expenditures and the neighborhood context inside the HHF
Zones that the dollars were spent in:
 A total of 3,739 demolitions occurred inside the HHF Zones between April 1st, 2014 (Q2
2014) and March 31st, 2015 (Q1 2015).
o A total of 3,302 of the HHF Zone demolitions occurring after policy
implementation were HHF funded demos (roughly 88.3%).
 The total number of occupied residential units inside the HHF Zones as of Q1 2015 is
112,763 occupied units.
o 43.3% of total occupied residential homes (48,848) had an HHF demolition occur
within 500 feet over the study time period.
 The average cost of an individual HHF demolition is $14,855 as of June 30th, 2015.
 The average price of “willing buyer/willing seller” sales of single-family homes inside the
HHF Zones after the implementation of the HHF Blight Elimination programs is $26,336.
7
See Appendices 2-8 for detailed tables that provide all relevant observational summary statistics for inside/outside HHF Zones,
before/after implementation of the HHF Blight Elimination Program, as well as some slicing up of the sub-marketing regime used for
the study (can be viewed via map in Appendix 9). See Appendix 1 for write-up explaining that summary statistics presented in a
hedonic modeling study can, if their function in the study is misunderstood, often appear to be at odds with the findings of the
study.
8
Total demolitions inside HHF Zones between April 1st, 2014 – March 31st, 2015 and average HHF demolition cost provided by the
Detroit Land Bank Authority on June 29th, 2015.
9
TABLE 1: Hedonic Regression of Single-family Homes Sales in Detroit, MI, Q2 2013 - Q1 2015 (8,386 Observations)
Dependent Variable: Natural Log of Sales Price
Citywide
Inside HHF
Outside HHF
(8,386
Obs.)
Spatial Variables: Activity Within 500 Feet of Home Sold*
Zones (5,526
Obs.)
Zones (2,860
Obs.)
# Residential Unoccupied Tax Foreclosable Structures
-0.029***
-0.028***
-0.029***
# Residential Vacant Lots
-0.005**
-0.002
-0.008**
# Occupied Residential Structures
0.003***
0.003***
0.003***
Violent Crime Rate
-0.211
-0.666
0.251
Property Crime Rate
1.019**
0.416
2.021
# Properties Sold for >$25,000
0.097***
0.103***
0.089***
Ln of Average Sales Price of Nearest Six Neighbors That Sold in Previous Time Period
0.207***
0.144***
0.295***
Policy Variables: HHF Indicators Associated With Home Sold
Indicator of Sale Occurring Inside HHF Zones
0.145***
Indicator of Sale Occurring After HHF Implementation
0.11**
0.138***
0.047
Additional Impact of Nearby Unoccupied Tax Foreclosable Structure After HHF
-0.012*
-0.014*
-0.004
Additional Impact of Nearby Residential Vacant Lot After HHF Implementation
Physical Variables: Structural Attributes of Home Sold
-0.004
-0.006
-0.002
Home Sold as Tax Foreclosable
-0.582***
-0.548***
-0.639***
Square Footage of Home/100
0.035***
0.037***
0.029***
# Bathrooms
0.086*
0.052
0.137
# Fire Places
0.018
0.027
0.027
Brick Construction Indicator
0.207***
0.212***
0.223***
Square Footage of Porch/100
0.025
0.035*
0.016
Air Conditioning Indicator
0.057*
0.003
0.162**
Age of Home When Sold
Submarket Variables: Block Group Level U.S. Census Median Income, 5-Year Est.
Submarket 1: Middle Median Income Housing Market
-0.005***
-0.005***
-0.005**
Submarket 2: Lowest Median Income Housing Market
-0.104***
-0.111**
-0.130*
Submarket 3: Highest Median Income Housing Market
0.269***
0.291***
0.300
Submarket 4: Low Median Income Housing Market
Seasonal Variables: Indicators of the Quarter A Home Sold In
Home Sold in First Quarter
-0.072**
-0.080**
-0.088
Home Sold in Second Quarter
0.039
0.020
0.081
Home Sold in Third Quarter
0.007
0.021
-0.012
Home Sold in Fourth Quarter
Sale Type Variables: Deed Transfer Type
-0.035
-0.017
-0.074
Arms Length Sale Indicator
0.03
-0.006
0.091
Quit Claim Deed Indicator
-0.818***
-0.911***
-0.640***
Warranty Deed Indicator
0.246***
0.173**
0.381***
Land Contract Indicator
0.565***
0.500***
0.689***
Real Estate Owned (REO) Indicator
-0.515***
-0.523***
-0.494***
Investment Property Indicator
0.453***
Hedonic Regression Constant
7.13***
Hedonic Regression R-Squared
0.324
Statistical Significance Key: *** = Less than 0.001; ** = Less Than 0.01; * = Less than 0.05.
0.409***
7.939***
0.296
0.551***
6.302***
0.337
Pulled Indicator as Reference
Pulled Indicator as Reference
10
The models performed marginally, with R-Squared values hovering around 0.30. This suggests
that roughly 30% of the variation in the natural log of single-family home sales prices between
Q2 2013 and Q1 2015 in Detroit are explained by this causal modeling exercise. The hedonic
method used to specify the models in this study assume a rational market existing in perfect
competition. Although great care was taken in identifying sales observations that include willing
buyers and sellers of homes in Detroit, the market in Detroit is complex, is significantly impacted
by economic hardship, speculation and tax foreclosure and changes drastically from block to
block. These and many other factors provide a difficult space for market-based econometric
modeling in Detroit. Nevertheless, even without its theoretical foundation based on market
equilibrium, the model provides an adequate “empirical” representation of a range of
determining factors in the house sales prices. A Chow test (Chow, 1960) was performed to test
the difference in significance between the HHF and non-HHF regimes model versions. The global
test showed high statistically significant difference between the two regimes, while independent
variables were weakly different from one another – see Chow Test results in Appendix 16 for
details.
With all weak-market and Detroit-specific modeling issues taken into account, the models in this
study are quite stable and performed consistently in terms of the significance and magnitude of
spatial, policy and other standard model variable coefficients. It is therefore reasonable to
assume this model’s variables and associated coefficients would perform with stability and
consistency in any future Detroit real estate market modeling. The relatively low R-Squared
limits the value of the results in a purely predictive exercise. Nevertheless, the strong
significance of the coefficient estimates for several of the important policy variables provides
the necessary basis for insight and decision support to better understand the performance
outcomes and neighborhood effect of the rapid, targeted HHF Blight Elimination Program in
Detroit.
Interpretation of Key Empirical Findings
The spatial and policy variables are of significant interest in this exercise as they provide the
necessary insight and decision support to estimate the performance and impact of the HHF
Blight Elimination Program. The section below provides explanations of the value effect each
key individual variable has on single-family home values in Detroit’s real estate market. The
statements below are directly derived from Table 1, above.
Spatial Variable Interpretations
Residential Unoccupied Tax Foreclosable Structures Within 500 Feet of a Sale


Citywide: Regression findings suggest with high statistical significance that an
additional9 residential structure that is unoccupied and tax foreclosable within 500 feet
of a single-family home in Detroit will negatively impact home value by 2.9%.
Inside HHF Zone Regime Regression findings suggest with high statistical significance
that an additional residential structure that is unoccupied and tax foreclosable within
9
The word “additional” in economic research has a meaning slightly different that in normal English. When the word “additional” is
used in this study, it can refer to the first event in a series, while in normal English “additional” never refers to the first event. So,
when this study states “an additional residential structure” with certain characteristics, this can be referring to the first such
residential structure, or the second, or the third, etc., in a series.
11

500 feet of a single-family home inside HHF demolition Zones will negatively impact
home value by 2.8%.
Outside HHF Zone Regime: Regression findings suggest with high statistical significance
that an additional residential structure that is unoccupied and tax foreclosable within
500 feet of a single-family home outside HHF Zones will negatively impact home value
by 2.9%.
Residential Vacant Lots Within 500 Feet of a Sale



Citywide: Regression findings suggest with high statistical significance that an additional
residential vacant lot within 500 feet of a single-family home in Detroit will negatively
impact home value by 0.5%.
Inside HHF Zone Regime: Regression findings suggest that a residential vacant lot within
500 feet of a single-family home inside HHF Zones will have no significant impact on
housing value.
Outside HHF Zone Regime: Regression findings suggest with high statistical significance
that an additional residential vacant lot within 500 feet of a single-family home outside
HHF Zones will negatively impact home value by 0.8%.
Occupied Residential Structures Within 500 Feet of Sale



Citywide: Regression findings suggest with high statistical significance that an additional
occupied residential structure within 500 feet of a single-family home in Detroit will
positively impact home value by 0.3%.
Inside HHF Zone Regime: Regression findings suggest with high statistical significance
that an additional occupied residential structure within 500 feet of a single-family home
inside HHF Zones will positively impact home value by 0.3%.
Outside HHF Zone Regime: Regression findings suggest with high statistical significance
that an additional occupied residential structure within 500 feet of a single-family home
outside HHF Zones will positively impact home value by 0.3%.
Properties that Sold For Greater Than $25,000 Within 500 Feet



Citywide: Regression findings suggest with high statistical significance that an additional
home that sells for greater than $25,000 within 500 feet of a single-family home in
Detroit will increase single-family home sales by 9.7%.
Inside HHF Zone Regime: Regression findings suggest with high statistical significance
that an additional home that sells for greater than $25,000 within 500 feet of a singlefamily home inside HHF Zones will increase single-family home sales by 10.3%.
Outside HHF Zone Regime: Regression findings suggest with high statistical significance
that an additional home that sells for greater than $25,000 within 500 feet of a singlefamily home outside HHF Zones will increase single-family home sales by 8.9%.
12
Policy Variable Interpretations
Indicator of Sale Occurring Inside Targeted HHF Zones

Citywide: Regression findings suggest with high statistical significance that a singlefamily home selling inside HHF Zones will sell for 14.5% more than those outside HHF
Zones.
Indicator of Sale Occurring After HHF Demolition Program Implementation (Q2 2014 and later)



Citywide: Regression findings suggest with high statistical significance that a singlefamily home sale in Detroit occurring after the implementation of the HHF Demolition
Program sold with an 11% increase in value.
Inside HHF Zone Regime: Regression findings suggest with high statistical significance
that a single-family home sale inside HHF Zones that occurs after the implementation of
the HHF Demolition Program sold with a 13.8% increase in value.
Outside HHF Zone Regime: Regression findings suggest that a single-family home sale
outside HHF Zones that occurred after the implementation of the HHF Demolition
Program did not significantly impact value.
Interaction Variable of Unoccupied Tax Foreclosable Structures After HHF Demolition Program
Implementation (Q2 2014 and later)



Citywide: Regression findings suggest with statistical significance that an additional
residential unoccupied tax-foreclosable structure within 500 feet of a single-family
home sale has an additional negative impact of 1.2% on home value after the
implementation of the HHF program.
Inside HHF Zone Regime: Regression findings suggest with statistical significance that an
additional residential unoccupied tax foreclosable structure within 500 feet of a singlefamily home sale inside HHF Zones has an additional negative impact of 1.4% on home
value after the implementation of the HHF Demolition Program.
Outside HHF Zone Regime: Regression findings suggest no statistically significant impact
from an additional residential unoccupied tax foreclosable structure within 500 feet of a
single-family home sale outside HHF Zones after the implementation of the HHF
Demolition Program.
Interaction Variable of Residential Vacant Lots After HHF Demolition Program Implementation
(Q2 2014 and later)


Citywide: Regression findings suggest no statistically significant impact from an
additional residential vacant lot within 500 feet of a single-family home sale after the
implementation of the HHF program.
Inside HHF Zone Regime: Regression findings suggest with weak statistical significance
that an additional residential vacant lot within 500 feet of a single-family home sale
inside HHF Zones has an additional negative impact of 0.6% on home value after the
implementation of the HHF Demolition Program.
13

Outside HHF Zone Regime: Regression findings suggest no statistically significant impact
from an additional residential vacant lot within 500 feet of a single-family home sale
outside HHF Zones after the implementation of the HHF Demolition Program
14
Policy Implications
 Citywide model estimates a 14.5% increase in single-family home sale prices inside the
HHF Zones. This finding suggests that homes inside HHF Zone boundaries are selling for
more than outside, which may indicate proper targeting of HHF demolition funds given that
a program goal is to positively impact home equity.
 The Inside HHF Zone regime model strongly suggests that homes within HHF Zones sell for
up to 13.8% more within HHF zones and after implementation of the HHF Blight
Elimination Program. Meanwhile, single-family homes selling outside HHF Zones had no
significant increase in value associated with the post-HHF implementation variable. These
coefficient estimates suggest that home sales within HHF Zones after HHF Blight Elimination
Program implementation are better off. This strength could reasonably be attributed to
implementation of the HHF Blight Elimination Program. But further analysis is required for a
complete understanding of this finding. Study of the summary statistics in Appendix 1-7 can
provide greater insight on this question.
 The blighted structure interaction variable is significant in the HHF Zone regime model,
suggesting an increased home value impact associated with demolition of nearby blight
after the implementation of the HHF program. The aggregate negative impact from a
blighted structure within 500 feet inside HHF Zones after HHF implementation is 4.2%,
while an estimated negative value impact of only 2.8% exists before HHF implementation
inside the HHF Zones. This finding suggests that rapid, targeted demolition of nearby
blight causes remaining blight to be increasingly negative in its impact on nearby homes –
a potential signal that finishing the job of full eradication of blight may be best for the
stability of neighborhood home equity.
 Vacant lots have no significant negative impact on value within HHF Zones, suggesting a
positive value impact of 4.2% is available for nearby homes from demolition inside HHF
Zones. On the other hand, outside HHF Zones vacant lots have a negative effect on
surrounding home value, so that the aggregate positive value impact that is estimated as
available from demolition outside HHF Zones is only 2.1%, both before and after HHF
implementation.
In short, the observations contained in the paragraphs above suggest that the greatest home
value increases from demolition activity appear to be inside HHF Zones after the
implementation of the HHF Blight Elimination Program.
15
Study Limitations and Future Research
Now that D-STADS™ exists, there are several potential improvements of the hedonic model for
future research of Detroit’s housing market:






10
Extending the scope of local property characteristics, in particular with respect to
various accessibility measures (distance to “main activities”, access to transit, local
amenities such as parks, distance to schools/quality of schools, walk score, etc.);
Examining the potential of a multilevel modeling approach, utilizing extensive
neighborhood characteristics;
Exploring the sensitivity of the results to the “range of influence” of blighted locations
(e.g., beyond 500ft);
Refining the definition of submarkets10 (e.g., using clustering techniques, machine
learning) and assessing the sensitivity of the results to submarket “regimes”;
Further examining the sensitivity of the results to the specification of the space-time
dynamics of the market, e.g., demolitions in more than one quarter vs. current quarter,
the range of nearest neighbors (in the space-time lag);
Further examining the sensitivity of the results to different specifications for the spatial
spillover effects (e.g., same-quarter spatial lag in addition to space-time lag, spatial error
autocorrelation).
See Appendix 8 for reference to the submarket approach used in this modeling exercise.
16
References
Angrist, Joshua D. and Jorn-Steffen Pischke (2015). Mastering metrics, the path from cause to
effect. Princeton, NJ, Princeton University Press.
Anselin, Luc and Sergio Rey (2014). Modern spatial econometrics in practice. Chicago, IL, GeoDa
Press.
Anselin, Luc and Daniel Arribas-Bel (2013). Spatial fixed effects and spatial dependence in a
single cross-section. Papers in Regional Science 92, 3-17.
Anselin, Luc (1988). Spatial econometrics, methods and models. Dordrecht, Kluwer.
Campbell, J.Y., Giglio, S., Pathak, P., 2011. Forced sales and house prices. American Economic
Review 101, 2108–2132.
Chow, G. 1960. Tests of inequality between sets of coefficients in two linear regressions.
Econometrica 28, 591-605.
Griswold, Nigel. 2006. "The Impacts of Tax-Foreclosed Properties and Land Bank Programs on
Residential Housing Values in Flint, Michigan." Michigan State University Master’s Thesis.
Griswold, N. and Norris, P. 2007. Economic Impacts of Residential Property Abandonment and
the Genesee County Land Bank in Flint, Michigan. Land Policy Institute.
Griswold, N., Calnin. B., Schramm, M., Anselin, L. and P. Boehnlein. 2014. Estimating the Effect
of Demolishing Distressed Structures in Cleveland, OH, 2009-2013: Impacts on Real Estate Equity
and Mortgage-Foreclosure. Publication of the Western Reserve Land Conservancy: Thriving
Communities Institute.
Groves, J.R., Rogers, W.H., 2011. Effectiveness of RCA institutions to limit local externalities:
using foreclosure data to test covenant effectiveness. Land Economics 87, 559–581.
Hartley, D., 2010. The effect of foreclosures on nearby housing prices: supply or disamenity?
Federal Reserve Bank of Cleveland, Working Paper, pp. 10–11.
Harding, J.P., Rosenblatt, E., Yao, V.W., 2009. The contagion effect of foreclosed properties.
Journal of Urban Economics 66, 164–178.
Imbens, Guido W. and Donald B. Rubin (2015). Causal inference for statistics, social and
biomedical sciences, an introduction. New York, NY, Cambridge University Press.
Immergluck D. and Geoff Smith. 2006. The External Costs of Foreclosure: The Impact of Singlefamily Mortgage-foreclosures on Property Values. Housing Policy Debate. Vol. 17. pp. 57 – 79.
Kobie, T.F. 2009. Residential Foreclosures’ Impact on Nearby Single-Family Residential
17
Properties: A New Approach to the Spatial and Temporal Dimensions. Cleveland State University
Dissertation. 138 pp.
Lancaster, Kelvin J. 1966. A New Approach to Consumer Theory. Journal of Political Economy.
Vol. 74. pp. 132-56.
Lin, Z., Rosenblatt, E., Yao, V.W., 2009. Spillover effects of foreclosures on neighborhood
property values. Journal of Real Estate Finance and Economics 38, 387–407.
Leonard, T., Murdoch, J.C., 2009. The neighborhood effects of foreclosure. Journal of
Geographic Systems 11, 317–332.
Mikelbank, B.A., 2008. Spatial analysis of the impact of vacant, abandoned and foreclosed
properties. <http://www.clevelandfed.org/ CommunityDevelopment/publications/
SpatialAnalysisImpactVacantAbandonedForeclosedProperties.pdf>.
Rogers, W.H., Winter, W., 2009. The impact of foreclosures on neighboring housing sales.
Journal of Real Estate Research 31, 455–479.
Rogers, W.H., 2010. Declining foreclosure neighborhood effects over time. Housing Policy
Debate 20, 687–706.
Rosen, S. 1974. “Hedonic Prices and Implicit Markets: Product Differentiation in Pure
Competition.” The Journal of Political Economy. Vol. 82, No. 1. pp. 34 -55.
Schuetz, J., Been, V., Ellen, I.G., 2008. Neighborhood effects of concentrated mortgageforeclosures. Journal of Housing Economics 17, 306–319.
Simons, R., Quercia, R., and Maric, I. 1998. The Value Impact of New Residential Construction
and Neighborhood Disinvestment on Residential Sales Price, Journal of Real Estate Research. Vol
15. pp. 147-61.
Whitaker, S., and T.J. Fitzpatrick. 2013. Deconstructing distressed-property spillovers: The
effects of vacant, tax-delinquent, and foreclosed properties in housing submarkets. Journal of
Housing Economics. Vol. 22, pp. 79-91.
18
Appendices
19
1. Cautionary Note on Summary Statistics
The summary statistics in Appendices 2-8 provide several cuts of the 8,386 homes sales observations that were used in
this hedonic modeling exercise. Given that the sales observations were cut into many unique spatial zones, some value
typologies (i.e. high value sales or low value sales observations) may be over- or underrepresented in the summary
statistics tables. For example, in Appendix 5 the average sales prices in the high income housing submarket outside the
HHF Zones are much higher than those inside the HHF Zones. Given that the hedonic model findings suggest that homes
within the HHF Zones after the implementation of the HHF Blight Elimination Program sell for up to 13.8% more than
those outside the HHF Zones, these summary statistics may be confusing. The reason for this potential confusion is that
the HHF Zones are residential areas with relatively consistent housing market types – i.e standard single family homes in
consistent markets. So even the higher-end of these markets have sales only in the $50,000 range. On the other hand,
outside the HHF Zones the high-end market includes many higher values single-family homes – i.e. hundreds of
thousands of dollars. In short, the model suggesting a 13.8% higher sales value for homes selling in HHF Zones is
consistent and correct, even though summary statistics may lead a reader to think otherwise because extreme high end
sales do not occur within HHF Zones. Other, similar misunderstandings can arise.
20
2. Summary Statistics of Average Single-family Homes: Inside/Outside HHF Zones & Before/After HHF Implementation
Citywide
Count of Single-family Home Sales
Average Sales Price
Average Sales Price of Recent Nearby Sales
Percent Sold in First Quarter
Percent Sold in Second Quarter
Percent Sold in Third Quarter
Percent Sold in Fourth Quarter
Average Square Footage
Average Age of Home When Sold
Percent Sold With Air Conditioning
Average Number of Bathrooms
Percent Sold Made of Brick
Average Number of Fire Places
Average Porch Square Footage
Percent Sold as Tax Foreclosable
Percent Sold as Arms Length Sale
Percent Sold as Quit Claim Deed
Percent Sold as Warranty Deed
Percent Sold as Land Contract
Percent Sold as Real Estate Owned (REO)
Percent Sold as Investment Properties
Average Number Homes Sold >$25K within 500 Feet
Average Number of Unoccupied Tax Foreclosable Properties
within 500 Feet
Average Number of Occupied Homes within 500 Feet
Average Number of Tax Foreclosable Homes within 500 Feet
Average Number of Vacant Residential Lots within 500 Feet
Average Number of Violent Crimes within 500 Feet
Average Number of Property Crimes within 500 Feet
Before
HHF
After
HHF
8,386
4,637
3,749
$22,531 $20,670 $24,833
$13,869 $12,748 $15,393
21.7%
23.4%
19.6%
27.5%
30.2%
24.0%
26.5%
25.0%
28.2%
24.4%
21.4%
28.2%
1,087
1,083
1,093
74
74
75
14.8%
15.2%
14.3%
1.19
1.18
1.19
68.5%
67.7%
69.5%
0.4
0.4
0.4
105
103
106
12.6%
11.3%
14.2%
4.4%
3.9%
5.0%
1.4%
0.6%
2.3%
3.8%
3.0%
4.6%
3.8%
3.9%
3.7%
16.1%
23.4%
7.1%
16.1%
19.6%
11.7%
0.3
0.3
0.3
6
77
25
8
0.9
2.7
6
77
27
8
1.0
3.0
5
77
21
9
0.9
2.3
Change
Outside
HHF
Inside
HHF
-19.2%
2,860
5,526
20.1% $19,637 $24,029
$2,645 $11,522 $15,265
-3.8%
21.7%
21.6%
-6.2%
28.8%
26.7%
3.2%
26.5%
26.5%
6.8%
22.9%
25.2%
0.9%
1,061
1,101
1.4%
74
74
-0.9%
13.7%
15.4%
0.9%
1.16
1.20
1.8%
63.8%
70.8%
4.5%
0.3
0.4
2.4%
102
106
2.9%
14.1%
11.8%
1.1%
4.7%
4.3%
1.8%
1.2%
1.4%
1.6%
3.7%
3.8%
-0.2%
3.9%
3.8%
-16.3%
17.4%
15.4%
-7.9%
16.6%
15.8%
-3.9%
0.2
0.3
-18.5%
0.4%
-24.1%
3.8%
-7.8%
-23.3%
21
6
72
26
11
0.9
2.4
5
79
24
7
0.9
2.9
Outside HHF
Inside HHF
Difference Before
After
Before
After
Change
HHF
HHF
HHF
HHF
93.2%
1,628
1,232 -24.3%
3,009
2,517
22.4% $18,028 $21,763
20.7% $22,099 $26,336
$3,743 $10,748 $12,631 $1,882 $13,980 $16,958
-0.1%
22.4%
20.9%
-1.4%
23.9%
18.9%
-2.1%
31.9%
24.8%
-7.1%
29.3%
23.7%
0.0%
24.6%
29.0%
4.3%
25.3%
27.9%
2.2%
21.1%
25.3%
4.2%
21.5%
29.6%
3.8%
1,054
1,070
1.5%
1,099
1,104
0.6%
73
75
1.9%
74
75
1.6%
14.1%
13.3%
-0.8%
15.9%
14.8%
3.2%
1.16
1.17
0.7%
1.20
1.21
7.0%
62.8%
65.2%
2.3%
70.3%
71.6%
23.5%
0.3
0.4
9.1%
0.4
0.4
3.7%
100
105
5.6%
105
106
-2.3%
14.7%
13.2%
-1.5%
9.4%
14.7%
-0.4%
3.4%
6.4%
3.0%
4.2%
4.4%
0.2%
0.6%
2.1%
1.6%
0.6%
2.5%
0.0%
3.1%
4.6%
1.6%
3.0%
4.6%
-0.1%
4.7%
2.8%
-2.0%
3.5%
4.1%
-2.0%
25.0%
7.5% -17.5%
22.5%
7.0%
-0.8%
19.3%
13.0%
-6.4%
19.7%
11.1%
61.3%
0.2
0.2 -22.5%
0.3
0.3
-12.4%
9.8%
-9.2%
-31.5%
4.2%
18.1%
6
72
29
11
0.9
2.7
6
72
22
11
0.9
2.1
-14.6%
0.0%
-22.9%
-1.3%
-0.8%
-24.4%
6
79
27
7
1.0
3.2
5
79
20
8
0.9
2.5
Change
-16.4%
19.2%
$2,978
-5.1%
-5.6%
2.6%
8.1%
0.4%
1.2%
-1.1%
0.9%
1.3%
2.0%
0.8%
5.3%
0.2%
1.9%
1.6%
0.7%
-15.6%
-8.6%
1.4%
-20.4%
0.2%
-24.6%
9.5%
-11.2%
-23.3%
3. Summary Stats of Average Single-family Homes: Housing Submarkets Based on Block-Group Level Median Income
High Income
Submarket
Count of Single-family Home Sales
Average Sales Price
Average Sales Price of Recent Nearby Sales
Percent Sold in First Quarter
Percent Sold in Second Quarter
Percent Sold in Third Quarter
Percent Sold in Fourth Quarter
Average Square Footage
Average Age of Home When Sold
Percent Sold With Air Conditioning
Average Number of Bathrooms
Percent Sold Made of Brick
Average Number of Fire Places
Average Porch Square Footage
Percent Sold as Tax Foreclosable
Percent Sold as Arms Length Sale
Percent Sold as Quit Claim Deed
Percent Sold as Warranty Deed
Percent Sold as Land Contract
Percent Sold as Real Estate Owned (REO)
Percent Sold as Investment Properties
Average Number Homes Sold >$25K within 500 Feet
Average # of Unoccupied Tax Foreclosable Properties within 500 Feet
Average Number of Occupied Homes within 500 Feet
Average Number of Tax Foreclosable Homes within 500 Feet
Average Number of Vacant Residential Lots within 500 Feet
Average Number of Violent Crimes within 500 Feet
Average Number of Property Crimes within 500 Feet
300
$52,488
$29,272
22.3%
26.3%
20.7%
30.7%
1,507
76
23.0%
1.53
83.3%
0.71
120
14.3%
8.0%
2.0%
3.0%
4.3%
17.7%
11.3%
0.5
2
73
15
5
0.6
2.3
22
Middle High
Income
Submarket
2,057
$27,672
$17,417
19.9%
26.7%
26.6%
26.7%
1,192
73
19.1%
1.27
84.0%
0.53
106
11.0%
4.8%
1.0%
3.2%
3.2%
15.8%
17.4%
0.3
4
79
21
5
0.8
2.8
Middle Low
Income
Submarket
3,731
$20,004
$12,920
22.7%
27.4%
27.2%
22.7%
1,040
74
13.9%
1.15
69.2%
0.37
101
13.5%
3.6%
1.3%
4.1%
4.2%
16.7%
17.4%
0.3
6
77
26
8
1.0
2.8
Low Income
Submarket
2,298
$18,121
$11,510
21.4%
28.4%
25.9%
24.3%
1,016
77
11.4%
1.13
51.4%
0.26
106
12.3%
5.0%
1.7%
3.9%
3.7%
15.2%
13.3%
0.2
7
74
27
13
1.0
2.6
4. Summary Statistics of Average Single-family Homes: Median Income Housing Submarkets Inside/Outside HHF Zones
Count of Single-family Home Sales
Average Sales Price
Percent Sold in First Quarter
Percent Sold in Second Quarter
Percent Sold in Third Quarter
Percent Sold in Fourth Quarter
Average Square Footage
Average Age of Home When Sold
Percent Sold With Air Conditioning
Average Number of Bathrooms
Percent Sold Made of Brick
Average Number of Fire Places
Average Porch Square Footage
Percent Sold as Tax Foreclosable
Percent Sold as Arms Length Sale
Percent Sold as Quit Claim Deed
Percent Sold as Warranty Deed
Percent Sold as Land Contract
Percent Sold as Real Estate Owned (REO)
Percent Sold as Investment Properties
Average Number Homes Sold >$25K within 500 Feet
Average Number of Unoccupied Tax Foreclosable
Properties within 500 Feet
Average Number of Occupied Homes within 500 Feet
Average # of Tax Foreclosable Homes within 500 Feet
Average # of Vacant Residential Lots within 500 Feet
Average Number of Violent Crimes within 500 Feet
Average Number of Property Crimes within 500 Feet
High Income Submarket
Outside
Inside HHF Difference
HHF
22
278
1163.6%
$148,879
$44,860
-69.9%
22.7%
22.3%
-0.4%
27.3%
26.3%
-1.0%
13.6%
21.2%
7.6%
36.4%
30.2%
-6.1%
2,615
1,419
-45.7%
84
76
-9.6%
31.8%
22.3%
-9.5%
2.77
1.43
-48.6%
77.3%
83.8%
6.5%
0.2
0.8
230.8%
144
118
-18.3%
13.6%
14.4%
0.8%
4.5%
8.3%
3.7%
4.5%
1.8%
-2.7%
4.5%
2.9%
-1.7%
0.0%
4.7%
4.7%
13.6%
18.0%
4.3%
9.1%
11.5%
2.4%
0.2
0.6
214.6%
4
38
14
11
0.1
1.0
2
76
15
4
0.7
2.4
-48.3%
102.4%
7.2%
-63.9%
616.2%
143.5%
Middle High Income Submarket
Outside
Inside HHF Difference
HHF
550
1,507
174.0%
$26,147
$28,228
8.0%
19.6%
20.0%
0.4%
26.9%
26.6%
-0.3%
27.6%
26.3%
-1.4%
25.8%
27.1%
1.3%
1,152
1,207
4.8%
73
73
-0.8%
16.2%
20.2%
4.0%
1.24
1.28
2.9%
74.2%
87.6%
13.4%
0.5
0.6
14.9%
107
106
-1.4%
13.3%
10.2%
-3.1%
5.8%
4.4%
-1.4%
0.7%
1.1%
0.3%
2.7%
3.3%
0.6%
2.4%
3.5%
1.1%
14.7%
16.3%
1.5%
16.2%
17.8%
1.6%
0.3
0.4
32.4%
4
78
21
6
0.8
2.6
23
4
80
21
4
0.8
2.9
3.9%
3.1%
-0.4%
-31.9%
9.1%
9.1%
Middle Low Income Submarket
Outside
Inside HHF Difference
HHF
1,432
2,299
60.5%
$17,667
$21,459
21.5%
22.8%
22.7%
-0.1%
29.1%
26.3%
-2.7%
26.8%
27.4%
0.6%
21.4%
23.5%
2.2%
1,021
1,051
2.9%
73
74
1.1%
13.6%
14.0%
0.4%
1.14
1.17
2.7%
65.1%
71.8%
6.7%
0.3
0.4
21.8%
100
102
2.3%
14.5%
12.9%
-1.6%
4.5%
3.1%
-1.4%
1.2%
1.4%
0.2%
3.9%
4.2%
0.3%
5.0%
3.7%
-1.3%
17.9%
16.0%
-2.0%
18.3%
16.9%
-1.4%
0.2
0.3
39.5%
6
74
27
10
1.0
2.5
6
79
25
7
1.0
2.9
-6.1%
7.1%
-5.8%
-26.6%
-0.4%
18.9%
Low Income Submarket
Outside
Inside HHF Difference
HHF
856
1,442
68.5%
$15,428
$19,720
27.8%
21.4%
21.4%
0.1%
29.7%
27.7%
-2.0%
25.6%
26.1%
0.5%
23.4%
24.8%
1.5%
1,029
1,008
-2.0%
76
77
1.5%
11.9%
11.2%
-0.8%
1.12
1.13
1.3%
54.8%
49.4%
-5.4%
0.3
0.3
-7.9%
101
109
8.1%
13.9%
11.4%
-2.5%
4.4%
5.3%
0.8%
1.5%
1.7%
0.2%
4.1%
3.7%
-0.3%
3.2%
4.0%
0.9%
18.5%
13.2%
-5.2%
14.3%
12.7%
-1.6%
0.1
0.3
111.9%
7
66
28
15
0.9
2.3
6
79
26
11
1.1
2.9
-12.9%
19.0%
-10.0%
-24.5%
17.8%
25.2%
5. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in High Income Submarket
Count of Single-family Home Sales
Average Sales Price
Percent Sold in First Quarter
Percent Sold in Second Quarter
Percent Sold in Third Quarter
Percent Sold in Fourth Quarter
Average Square Footage
Average Age of Home When Sold
Percent Sold With Air Conditioning
Average Number of Bathrooms
Percent Sold Made of Brick
Average Number of Fire Places
Average Porch Square Footage
Percent Sold as Tax Foreclosable
Percent Sold as Arms Length Sale
Percent Sold as Quit Claim Deed
Percent Sold as Warranty Deed
Percent Sold as Land Contract
Percent Sold as Real Estate Owned (REO)
Percent Sold as Investment Properties
Average Number Homes Sold >$25K within 500 Feet
Average # of Unoccupied Tax Foreclosable Properties within 500 Feet
Average Number of Occupied Homes within 500 Feet
Average Number of Tax Foreclosable Homes within 500 Feet
Average Number of Vacant Residential Lots within 500 Feet
Average Number of Violent Crimes within 500 Feet
Average Number of Property Crimes within 500 Feet
Outside HHF
Before HHF After HHF
13
9
$111,987
$202,167
7.7%
44.4%
30.8%
22.2%
15.4%
11.1%
46.2%
22.2%
2,461
2,838
86
81
23.1%
44.4%
2.42
3.28
84.6%
66.7%
0.4
0.0
125
171
0.0%
33.3%
7.7%
0.0%
0.0%
11.1%
0.0%
11.1%
0.0%
0.0%
15.4%
11.1%
15.4%
0.0%
0.2
0.2
5
3
40
34
17
9
12
9
0.2
0.0
0.9
1.1
24
Change
-30.8%
80.5%
36.8%
-8.5%
-4.3%
-23.9%
15.3%
-5.0%
21.4%
35.3%
-17.9%
-100.0%
36.8%
33.3%
-7.7%
11.1%
11.1%
0.0%
-4.3%
-15.4%
44.4%
-46.9%
-16.9%
-45.8%
-25.1%
-100.0%
20.4%
Inside HHF
Before HHF After HHF Change
148
130 -12.2%
$38,380
$52,237
36.1%
22.3%
22.3%
0.0%
33.1%
18.5% -14.6%
20.3%
22.3%
2.0%
24.3%
36.9%
12.6%
1,441
1,395
-3.2%
74
77
4.1%
23.6%
20.8%
-2.9%
1.47
1.38
-6.3%
85.1%
82.3%
-2.8%
0.7
0.8
10.6%
115
121
5.1%
6.8%
23.1%
16.3%
14.9%
0.8% -14.1%
0.7%
3.1%
2.4%
1.4%
4.6%
3.3%
2.7%
6.9%
4.2%
24.3%
10.8% -13.6%
18.2%
3.8% -14.4%
0.5
0.6
27.5%
2
2
10.5%
76
76
-0.3%
16
13 -19.2%
3
5
48.7%
0.6
0.7
31.5%
2.6
2.3 -12.1%
6. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in Middle-High Income Submarket
Count of Single-family Home Sales
Average Sales Price
Percent Sold in First Quarter
Percent Sold in Second Quarter
Percent Sold in Third Quarter
Percent Sold in Fourth Quarter
Average Square Footage
Average Age of Home When Sold
Percent Sold With Air Conditioning
Average Number of Bathrooms
Percent Sold Made of Brick
Average Number of Fire Places
Average Porch Square Footage
Percent Sold as Tax Foreclosable
Percent Sold as Arms Length Sale
Percent Sold as Quit Claim Deed
Percent Sold as Warranty Deed
Percent Sold as Land Contract
Percent Sold as Real Estate Owned (REO)
Percent Sold as Investment Properties
Average Number Homes Sold >$25K within 500 Feet
Average # of Unoccupied Tax Foreclosable Properties within 500 Feet
Average Number of Occupied Homes within 500 Feet
Average Number of Tax Foreclosable Homes within 500 Feet
Average Number of Vacant Residential Lots within 500 Feet
Average Number of Violent Crimes within 500 Feet
Average Number of Property Crimes within 500 Feet
Outside HHF
Before HHF After HHF
274
276
$23,284
$28,988
21.9%
17.4%
28.1%
25.7%
26.3%
29.0%
23.7%
27.9%
1,151
1,153
72
75
15.7%
16.7%
1.24
1.25
75.2%
73.2%
0.5
0.4
107
108
12.8%
13.8%
5.5%
6.2%
0.0%
1.4%
0.7%
4.7%
2.6%
2.2%
23.7%
5.8%
17.9%
14.5%
0.3
0.3
4
4
79
76
24
18
6
7
0.8
0.8
3.0
2.3
25
Change
0.7%
24.5%
-4.5%
-2.4%
2.7%
4.2%
0.2%
4.2%
1.0%
1.0%
-2.0%
-12.1%
0.5%
1.0%
0.7%
1.4%
4.0%
-0.4%
-17.9%
-3.4%
-0.7%
-15.4%
-3.7%
-25.6%
11.9%
-4.0%
-21.3%
Inside HHF
Before HHF After HHF Change
808
699 -13.5%
$25,987
$30,819
18.6%
22.0%
17.7%
-4.3%
30.3%
22.3%
-8.0%
24.9%
27.9%
3.0%
22.8%
32.0%
9.3%
1,191
1,226
3.0%
72
74
2.4%
21.0%
19.2%
-1.9%
1.25
1.31
4.9%
87.5%
87.7%
0.2%
0.6
0.5
-1.7%
107
105
-1.9%
7.4%
13.4%
6.0%
5.2%
3.4%
-1.8%
0.2%
2.0%
1.8%
2.2%
4.6%
2.4%
3.6%
3.3%
-0.3%
25.5%
5.6% -19.9%
22.9%
11.9% -11.0%
0.4
0.3 -20.4%
5
4 -13.0%
80
79
-1.3%
24
18 -23.2%
4
5
33.8%
0.9
0.7 -18.1%
3.3
2.4 -26.5%
7. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in Middle-Low Income Submarket
Count of Single-family Home Sales
Average Sales Price
Percent Sold in First Quarter
Percent Sold in Second Quarter
Percent Sold in Third Quarter
Percent Sold in Fourth Quarter
Average Square Footage
Average Age of Home When Sold
Percent Sold With Air Conditioning
Average Number of Bathrooms
Percent Sold Made of Brick
Average Number of Fire Places
Average Porch Square Footage
Percent Sold as Tax Foreclosable
Percent Sold as Arms Length Sale
Percent Sold as Quit Claim Deed
Percent Sold as Warranty Deed
Percent Sold as Land Contract
Percent Sold as Real Estate Owned (REO)
Percent Sold as Investment Properties
Average Number Homes Sold >$25K within 500 Feet
Average # of Unoccupied Tax Foreclosable Properties within 500 Feet
Average Number of Occupied Homes within 500 Feet
Average Number of Tax Foreclosable Homes within 500 Feet
Average Number of Vacant Residential Lots within 500 Feet
Average Number of Violent Crimes within 500 Feet
Average Number of Property Crimes within 500 Feet
Outside HHF
Before HHF After HHF
844
588
$16,842
$18,852
23.2%
22.1%
32.0%
24.8%
25.1%
29.3%
19.7%
23.8%
1,015
1,031
73
74
14.8%
11.9%
1.14
1.13
63.0%
68.0%
0.3
0.4
96
105
16.1%
12.2%
2.8%
6.8%
0.7%
1.9%
3.2%
4.9%
6.4%
2.9%
24.6%
8.3%
20.6%
15.0%
0.3
0.2
6
6
74
75
29
23
10
10
0.9
1.0
2.7
2.1
26
Change
-30.3%
11.9%
-1.1%
-7.2%
4.1%
4.1%
1.6%
1.3%
-2.9%
-1.0%
5.0%
21.0%
8.6%
-3.9%
4.0%
1.2%
1.7%
-3.5%
-16.3%
-5.7%
-39.3%
-14.2%
1.1%
-23.1%
-4.7%
2.4%
-24.2%
Inside HHF
Before HHF After HHF Change
1,262
1,037 -17.8%
$19,931
$23,319
17.0%
26.1%
18.5%
-7.6%
27.8%
24.5%
-3.3%
24.2%
31.3%
7.1%
21.8%
25.7%
3.9%
1,051
1,052
0.0%
74
74
0.6%
13.5%
14.8%
1.3%
1.17
1.16
-0.5%
70.7%
73.1%
2.4%
0.4
0.4
2.9%
101
103
2.2%
11.6%
14.5%
2.9%
2.4%
4.0%
1.6%
0.7%
2.3%
1.6%
3.0%
5.6%
2.6%
3.4%
4.1%
0.6%
22.7%
7.8% -14.9%
20.4%
12.5%
-7.9%
0.3
0.3
4.0%
6
5 -25.6%
79
80
1.4%
28
21 -25.4%
7
7
1.7%
1.0
0.9
-7.4%
3.3
2.5 -23.2%
8. Summary Statistics of Average Homes: Inside/Outside HHF & Before/After HHF in Low Income Submarket
Outside HHF
Before HHF After HHF Change
497
359
-27.8%
$14,687 $16,454
12.0%
21.5%
21.2%
-0.4%
33.8%
24.0%
-9.8%
23.1%
29.0%
5.8%
21.5%
25.9%
4.4%
1,031
1,026
-0.5%
75
77
1.7%
11.7%
12.3%
0.6%
1.12
1.12
0.1%
55.1%
54.3%
-0.8%
0.3
0.3
5.1%
100
102
2.1%
13.9%
13.9%
0.0%
3.2%
6.1%
2.9%
0.6%
2.8%
2.2%
4.2%
3.9%
-0.3%
3.2%
3.1%
-0.2%
26.6%
7.2%
-19.3%
18.1%
8.9%
-9.2%
0.1
0.1
-9.7%
8
7
-9.3%
66
66
-0.3%
31
25
-18.1%
14
15
5.5%
0.9
0.9
-0.6%
2.6
1.8
-29.3%
Count of Single-family Home Sales
Average Sales Price
Percent Sold in First Quarter
Percent Sold in Second Quarter
Percent Sold in Third Quarter
Percent Sold in Fourth Quarter
Average Square Footage
Average Age of Home When Sold
Percent Sold With Air Conditioning
Average Number of Bathrooms
Percent Sold Made of Brick
Average Number of Fire Places
Average Porch Square Footage
Percent Sold as Tax Foreclosable
Percent Sold as Arms Length Sale
Percent Sold as Quit Claim Deed
Percent Sold as Warranty Deed
Percent Sold as Land Contract
Percent Sold as Real Estate Owned (REO)
Percent Sold as Investment Properties
Average Number Homes Sold >$25K within 500 Feet
Average # of Unoccupied Tax Foreclosable Properties within 500 Feet
Average Number of Occupied Homes within 500 Feet
Average Number of Tax Foreclosable Homes within 500 Feet
Average Number of Vacant Residential Lots within 500 Feet
Average Number of Violent Crimes within 500 Feet
Average Number of Property Crimes within 500 Feet
27
Inside HHF
Before HHF After HHF Change
791
651 -17.7%
$18,540
$21,155
14.1%
22.6%
20.0%
-2.7%
30.0%
24.9%
-5.1%
28.2%
23.5%
-4.7%
19.2%
31.6%
12.4%
1,018
997
-2.0%
77
77
0.5%
13.0%
8.9%
-4.1%
1.14
1.13
-0.2%
49.2%
49.6%
0.4%
0.3
0.3
-1.0%
109
109
0.2%
8.6%
14.7%
6.1%
4.0%
6.8%
2.7%
0.6%
3.1%
2.4%
4.2%
3.2%
-0.9%
3.5%
4.6%
1.1%
19.0%
6.3% -12.7%
15.4%
9.4%
-6.1%
0.3
0.3
19.7%
7
6 -18.4%
79
79
0.2%
29
22 -24.2%
11
12
8.4%
1.2
1.0 -13.9%
3.2
2.5 -21.4%
9. Map of Submarkets for this Analysis: 5-Year Census Estimates of Median Income at Block Group Level
28
10. Map of Demolition Concentrations Before HHF Implementation (Q2 2013-Q1 2014)
29
11. Map of Demolition Concentrations After HHF Implementation (Q2 2014-Q1 2015)
30
12. Map of Average Single-Family Sale Price Concentrations Before HHF (Q2 2013-Q1 2014)
31
13. Map of Average Single-Family Sale Price Concentrations After HHF (Q2 2014 – Q1 2015)
32
14. Map of Unoccupied Tax Foreclosable Residential Properties in 2015 Q1
33
15. Map of Vacant Lot Concentrations in 2015 Q1
34
16. Map of Average Sales Price Change After HHF Blight Elimination Program Implementation
35
17. Chow Test Results - HHF Regimes Diagnostics
Variable name
# Residential Unoccupied Tax Foreclosable
Structures
# Residential Vacant Lots
# Occupied Residential Structures
Violent Crime Rate
Property Crime Rate
# Properties Sold for >$25,000
Degrees of
Freedom
Average Sales Price of Neighbors That Sold
in Previous Time Period
Indicator of Sale Occurring After HHF
Implementation
Additional Impact of Nearby Unoccupied
Tax Foreclosable Structure After HHF
Additional Impact of Nearby Residential
Vacant Lots After HHF Implementation
Home Sold as Tax Foreclosable
Square Footage of Home/100
# Bathrooms
# Fire Places
Brick Construction Indicator
Square Footage of Porch/100
Air Conditioning Indicator
Age of Home When Sold
Submarket 2: Lowest Median Income
Housing Market
Submarket 3: Highest Median Income
Housing Market
Submarket 4: Low Median Income Housing
Market
Home Sold in Second Quarter
Home Sold in Third Quarter
Home Sold in Fourth Quarter
Arms Length Sale Indicator
Quit Claim Deed Indicator
Warranty Deed Indicator
Land Contract Indicator
Real Estate Owned (REO) Indicator
Investment Property Indicator
Hedonic Regression Constant
GLOBAL TEST
36
Value
Probability
1
1
1
1
1
1
0.014
2.436
0.137
0.725
5.040
0.278
0.9066
0.1186
0.7109
0.3946
0.0248
0.5977
1
11.427
0.0007
1
1.492
0.2219
1
0.809
0.3685
1
1
1
1
1
1
1
1
1
0.693
1.397
0.646
1.070
0.000
0.032
0.364
7.017
0.015
0.4053
0.2373
0.4217
0.3010
0.9910
0.8570
0.5462
0.0081
0.9018
1
0.077
0.7812
1
0.001
0.9800
1
1
1
1
1
1
1
1
1
1
1
31
0.023
0.839
0.236
0.660
0.900
2.808
3.538
4.476
0.315
4.811
12.800
96.834
0.8792
0.3597
0.6271
0.4165
0.3427
0.0938
0.0600
0.0344
0.5746
0.0283
0.0003
0.0000