What has been the impact of Areas of Opportunity in Florida? Part 1 of 3

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NOTE: Someone kindly pointed out an error in my reading of the demographic data described below. The misreading led me to make a slight overstatement in the original version of this post. I have corrected the content below accordingly.

In 2016, the Florida Housing Finance Corporation (Florida Housing) began to implement its Area of Opportunity (AofO) strategy. The developments awarded funding under the 2016 geographic RFAs are currently in underwriting or under construction. Now we can begin to see whether the AofO strategy has made a difference. This is the first of three posts on the topic.

Below, I describe the process used to collect and compare information related to tax credit financed developments. If you do not share my enthusiasm for the technical minutia of housing policy, you may want to skip past that section. There’s also a brief discussion of how I chose to measure segregation. The results follow a description of the baseline data used for comparison.

In general, the AofO policy has not yet made much of a difference. The 2016 allocation yielded a somewhat more limited range of housing options for low-income families. Looking at Florida Housing designated Areas of Opportunity specifically, the early results of the policy change are encouraging. The properties funded in these tracts create affordable housing options in diverse neighborhoods with opportunities for upward mobility.

Methodology

Florida Housing has made a great leap forward in its use of GIS over the past year. Mercifully, the Corporation now collects Development Location Points (DLPs) in decimal form on the application for tax credits. This allows Florida Housing to list the latitude and longitude of proposed sites in Excel format on the applications submitted report. This is a fantastic improvement both operationally, and in terms of public transparency. Unfortunately, the DLPs for the 2016 applications are only available in Degree-Minute-Second (DMS) format on PDFs of the surveyor certification form.

We need to know where the new developments will be located in order to evaluate the neighborhood level indicators of community well being for the winning applications. Accordingly, I copied the DMS coordinates for each DLP from the PDFs into an Excel workbook. Then I converted the values to decimal form.  Adding these X,Y data to an ArcGIS map document, I intersected the points with 2010 Census Tracts using a 2016 TIGER shapefile. I then exported the records back to Excel. In this way, I compiled location information for the preliminary awards made under the three geographic RFAs. Florida Housing limited its AofO strategy to metropolitan counties. For this reason, I excluded the small county award made under RFA 2016-110, as it is located in a non-metropolitan county.

We are really after neighborhood-level data. “Neighborhood” is of course a tricky term. Census tracts are our best approximation. I downloaded data tables from American FactFinder for the 2016 5-year average of the American Community Survey (ACS). The data I used include poverty status (table S1701), median income (table S1903), employment (S2301), Hispanic identity by race (B03002), tenure (B25003), and SNAP utilization (S2201). I added each of these tables to the workbook. Additionally, I added the county level data for Hispanic identity by race, as well as poverty, for the counties which produced winning tax credit applications.

Finally, I associated the ACS data with each DLP. This was accomplished with a simple index/match function in Excel.[1] In this way, the relevant data from the ASC tables are associated with each DLP.

Measuring Segregation & Housing Choice

In my opinion, HUD defined and Florida Housing designated RECAPs are too generic to be of much use. Both designations are based on a minority concentration of 50.0% or more, and an incidence of poverty greater than or equal to 40.0%. This standard is based on academic research and federal policy that is national in scope. The consistent measure is a helpful starting point, but it is not sufficiently context-sensitive to tell us whether a state housing finance agency’s allocation criteria effectively further fair housing.

Florida is characterized by heterogeneous social geography. In Charlotte County, a tract with a minority population of 50.0% would indicate extreme racial/ethnic concentration. Such concentration could only be explained by a history of housing discrimination. However, in Miami-Dade County, and the other minority-majority counties in Florida, a minority population of only 50.0% would indicate a high concentration of non-Hispanic Whites.

Similarly, although a poverty rate of 40.0% is high anywhere in the state, the incidence of poverty varies considerably from county to county. For example, a census tract with a 20.0% incidence of poverty would be fairly typical in Miami-Dade County. However, the same value equates to 165.0% of the county-wide rate of poverty in Seminole County.

A high minority concentration is not in itself problematic, of course. It is the nexus of unusually high rates of poverty, and racial/ethnic isolation from the majority population, which perpetuates the pattern of residential segregation established during de jure segregation. I also do not mean to say that the existence of high incidences of poverty in non-Hispanic White majority areas is not a public concern. The point is that if the AofO strategy is at least in part intended to address the persistence of residential segregation, we must have some context-sensitive means of measuring it. Consequently, I identified tracts which are characterized by BOTH an incidence of poverty greater than 1.5x the county-wide incidence of poverty, AND a non-Hispanic White population less than half that of the county as a whole.

Setting a Baseline for Comparison

To establish a baseline, I identified properties awarded tax credits over several years prior to 2016. The Assisted Housing Inventory (maintained by the Shimberg Center for Housing Studies) lists 116 properties with affordability start dates between 2010 and 2015 located in the same counties as the 2016 awards. Fortunately, Shimberg reports the decimal form latitude and longitude coordinates for each site. I filtered out sites which were not listed as “ready for occupancy.”

Larger data sets are more stable and therefore more reliable. Comparing more than 100 properties funded over several years to a handful of properties funded in a single year may lead to incorrect conclusions. For this reason, I also evaluated the properties awarded 9% tax credits under the 2013 geographic RFAs. This allows a year-to-year comparison pre- and post- policy change.

Census Tracts – Preliminary Awards – 2016 Geographic 9% Tax Credit RFAs

Average of All Tracts Range
Labor Force Participation Rate 58.8% 43.1% – 77.1%
Tract Median Income $39,467 $9,693 – $64,444
Homeownership Rate 49.5% 2.5% – 84.1%
Incidence of Poverty 27.2% 4.4% – 75.9%
SNAP Utilization Rate 29.5% 6.8% – 85.3%
Total Minority

(Other than Non-Hispanic White)

60.6% 16.7% – 98.9%

A high percentage of the properties are located in census tracts with overlapping characteristics of poverty and racial/ethnic isolation. 5 of the 19 properties (26.3%) funded in 2016 are located in tracts characterized by BOTH an incidence of poverty greater than 1.5x the county-wide incidence of poverty, AND a non-Hispanic White population less than half that of the county as a whole.

Census Tracts – In Service – 2010-2015 9% Tax Credit Properties

Average of All Tracts Range
Labor Force Participation Rate 58.9% 26.1% – 77.1%
Tract Median Income $35,719 $13,660 – $110,521
Homeownership Rate 40.0% 3.7% – 92.2%
Incidence of Poverty 27.5% 2.5% – 56.0%
SNAP Utilization Rate 29.5% 2.8% – 64.4%
Total Minority

(Other than Non-Hispanic White)

67.4% 4.7% – 99.7%

35 of the 117 properties (29.9%) are located in tracts characterized by BOTH an incidence of poverty greater than 1.5x the county-wide incidence of poverty, AND a non-Hispanic White population less than half that of the county as a whole.

Census Tracts – 2013 Geographic 9% Tax Credit RFAs

Average of All Tracts Range
Labor Force Participation Rate 58.5% 35.5% – 79.5%
Tract Median Income $32,733 $16,219 – $60,882
Homeownership Rate 35.3% 2.9% – 82.3%
Incidence of Poverty 31.3% 15.1% – 61.2%
SNAP Utilization Rate 26.5% 5.5% – 49.2%
Total Minority

(Other than Non-Hispanic White)

62.6% 12.2% – 97.6%

5 of the 17 properties (29.4%) awarded funding under the 2013 geographic RFAs are located in tracts characterized by BOTH an incidence of poverty greater than 1.5x the county-wide incidence of poverty, AND a non-Hispanic White population less than half that of the county as a whole.

In General, there is No Meaningful Difference

In general, there are few meaningful differences between the 2016 allocation and Florida Housing’s competitive tax credit allocations over previous years. There are some positive results. The average tract median income has increased. Also, the average total minority concentration has decreased. Additionally, the 2016 sites are in tracts with a slightly lower average incidence of poverty, and higher average rates of home ownership. However, the ranges indicate a narrowing of neighborhood choice. During the 2010 – 2015 period, at least some properties were funded in tracts with home ownership rates as high as 92.2%, the highest incidence of poverty was 56.0%. In tracts containing sites awarded tax credits in 2016, the highest rate of home ownership is 84.1%; the highest incidence of poverty is 75.9%. Although the range of tract median incomes indicates that properties were funded in middle-income neighborhoods in 2016, at least a few were funded in more affluent census tracts during the earlier period.

But…Geographic AofOs Yield Measurably Better Spatial Outcomes

Of the 19 properties funded through the geographic RFAs in 2016, 4 are located in Florida Housing designated Areas of Opportunity. The proposed developments are located in 4 unique census tracts. The average rate of labor force participation is 64.4%; the average tract median income is $49,142; the average incidence of poverty is 17.4%; and, the average rate of home ownership is 65.3%. Interestingly, the average minority population is 70.8%; excluding the 2 Miami-Dade AofO tracts, it is 65.2%.  Nonetheless, non-Hispanic Whites are present in these communities. In the 2 Miami-Dade tracts, non-Hispanic Whites are present at more than double the county-wide rate in one tract, and just under the county-wide rate in the other. In the other AofO tracts, non-Hispanic Whites account for about one-third of the population.

Funded properties in geographic Areas of Opportunity offer affordable housing options in neighborhoods where both diversity, and upward mobility (in the form of work and home ownership), are normative. Moreover, although these are not the most exclusive neighborhoods, they are characterized by relatively moderate incidences of poverty.

The data are available here. Feel free to take a look and let me know in the comments section below if you see any potential improvements.

[1] =index(ACS_variable_array,match(tractnumber,acs_table_tract_array,exact_match))

Miami-Dade Tax Credit Applications

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In December of 2017, the Florida Housing Finance Corporation received 27 applications for competitive 9% tax credits in Miami-Dade County. The locations of the associated development sites are mapped below.

Comparing the Sites to the Average Miami-Dade Neighborhood

Let's take a closer look at the communities where theses sites are located.

In the census tracts containing sites for which developers submitted applications for competetive tax-credit funding:

  • The average poverty rate is 31.3% (with a range of 11.5% to 54.0%);
  • The average rate of households receiving food assistance benefits is 43.2% (with a range of 14.3% to 55.7%);
  • The average adult labor force participation rate is 58.9% (with a range of 42.8% to 74.1%); and
  • The average unemployment rate (among individuals over 16 years of age) is 13.6%.

Let’s put that all in context. For all populated census tracts in Miami-Dade County:

  • The average poverty rate is 20.0%;
  • The average rate of households receiving food assistance benefits is 25.0%;
  • The average adult labor force particiaption rate is 62.2%; and
  • The average unemployment rate (among individuals over 16 years of age) is 8.6%.

That means that on average, applications for tax credits are originating from census tracts where the incidence of poverty is 56.5% greater than the typical Miami-Dade County census tract; dependence on food assistance is 72.8% greater. Applications are originating from tracts where labor force participation is meaningfully less; and among those in the work force, unemployment is 58% greater than in the typical Miami-Dade County census tract.

Comparing Sites to Each Other

Among the 17 applications indicating that the development will serve the family demographic (i.e occupancy will not be limited to older persons), 3 are in census tracts where the rate of poverty is less than the average Miami-Dade County census tract. Another 8 are located in census tracts with rates of poverty less than a full standard deviation above the mean.

There are 20 applications which supposedly include participation by not-for-profit organizations (including one in which the true principal is a for-profit developer subject to a deferred prosecution agreement for lying to the Corporation about construction costs in order to enrich himself). Of these, 11 indicate that the property will serve the family demographic. In all likelihood, the funded deals will be among these applications.

All of these applications come from competent developers who can make it through underwriting, complete construction, and place the buildings in service on time. The buildings will all be physically indistinguishable from market rate housing.  All of the proposals are for concrete new construction. The most significant difference between these sites is social geography. Simply put, most are located in neighborhoods where poverty and dependence on public assistance are normative. A few are located in neighborhoods where poverty is not normative and upward mobility is more likely. To put this all in perspective, the peak unemployment rate in Florida during the Great Recession was 11.2%. That means that employment prospects in the neighborhoods producing tax credit applications during normal – even good times – are worse than most people experienced during the bleakest economic period in living memory.

Looking Ahead - Fair Housing Implications

The Corporation is in the process of updating the rules which guide the allocation of competitive resources, as well as the Qualified Allocation Plan. These guidelines will be operationalized in future RFAs. If Florida is committed to fair housing, it must recognize that where a person lives, especially during childhood, has a a significant impact on her opportunities for upward mobility. Our housing production programs should not be focused on warehousing the poor and keeping them in a position of dependence. Instead, we should expand the options available to low-income households and use the tax credit program to create housing opportunities in otherwise inaccessible neighborhoods.

 

Sources:

Florida Housing Finance Corporation RFA 2017-112; RFA 2017-112 Applications Submitted Report

American Community Survey 2016 5-yr Average tables: S2301; S2201; S1501; S1701

 

 

Who wins the most competitive tax credit awards in Florida?

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Who wins the most 9% tax credit awards in Florida? The question proved difficult to answer and some major caveats are in order. During the three-year period of 2014, 2015, and 2016, the Florida Housing Finance Corporation received 702 applications for 9% tax credits. The Florida Housing Board of Directors approved preliminary awards for 85 of these. Subsequent protests led to final orders and settlements which resulted in a few additional awards, as well as some of the 85 awarded deals being deemed ineligible. I used applications and preliminary awards for the information below, I’ll correct for additional awards and ineligible applications another time. The award amount comes from preliminary awards (based on figures provided by applicants), not from final credit underwriting reports.

During the period evaluated (2014, 2015, and 2016) it was often difficult to tell exactly who was applying for tax credits. Actual applicant entities are created for a specific property; similarly, the names of developer entities listed in applications often don’t reflect the parent organization. For this reason, I used the contact person listed in the application. One person who is especially active (and successful) changed organizations during the time-period evaluated. I decided to combine his applications from both employers, which include the same PHA co-developer. His former employer submitted additional applications, but did not win any preliminary awards. Obviously, in this case, the person and not the organization is critical.

Another name appeared as the contact for several applications, some of which were successful. The name is listed on CAHP’s website as a member associated with an organization with which I am unfamiliar. Searching for the applicant and developer names on Sunbiz, I found that McCormack Baron Salazar is part of the ownership structure for each of them. Accordingly, I added that organization to the developer column for those applications.

A few organizations submit applications under different contact names; I consolidated these into a single record for the rankings below. There are some practical difficulties gleaning this information from Florida Housing’s website. Several applicants write their names differently on different applications. For example, “Matthew” is sometimes “Matt”; “Elizabeth” is sometime “Liz”; and “Kimberly” is sometimes “Kim”. Similarly, “Jr.” and “Sr.” appear inconsistently and sometimes wihtout the period. As I imported these names into Excel, I made the names consistent, removed all periods, and trimmed excessive spaces (e.g. “John Q.   Doe” became “John Q Doe”). To keep things simple, I limited the evaluation to the Geographic RFAs, Preservation RFAs, and Revitalization RFAs.

The following RFAs were evaluated:

RFA 2014-104, RFA 2014-106, RFA 2014-114, RFA 2014-115, RFA 2014-116

RFA 2015-104, RFA 2015-106, RFA 2015-107, RFA 2015-108, RFA 2015-111, RFA 2015-113

RFA 2016-110, RFA 2016-113, RFA 2016-114, RFA 2016-116

I did not include solicitations which involved non-competitive funding, credits paired with SAIL, or other more narrowly focused RFAs (e.g. housing for persons with a disabling condition). In order to focus in on regular competitors, I filtered out all applicants who had submitted fewer than 4 applications over the three-year period.

The results are presented below in three ways: 1) highest percentage of wins (preliminary awards as a percentage of applications submitted); 2) total number of preliminary awards; and 3) total amount of the preliminary awards (annual tax credit requested in application).

Top 10 - Highest Percentage of Preliminary Awards

Contact PersonDeveloper(s)Number of AppsPreliminary AwardsPercentageTotal Request ($)
Robert G HoskinsNurock5240.00%4,671,000
Paula M Rhodes/Lori Harris/Brian D EvjenNorstar20630.00%8,049,398
Oscar A SolGreen Mills10330.00%4,275,000
Alexander B KissBanyan7228.57%3,020,000
Eileen M PopeTampa PHA/Banc of America4125.00%2,110,000
Eugenia AndersonGibraltar Development Partners/McCormack Baron Salazar4125.00%1,660,000
Hana K EskraGorman4125.00%365,009
James R Hoover/Stephen A FrickVestcor27622.22%8,860,852
Elizabeth WongAtlantic Pacific23521.74%10,467,111
Brianne E HeffnerSouthport47817.02%8,156,000

Top 10 - Number of Preliminary Awards

Contact PersonDeveloper(s)Number of AppsPreliminary AwardsPercentageTotal Request ($)
Brianne E HeffnerSouthport47817.02%8,156,000
Matthew RiegerHTG73810.96%13,358,687
David O DeutchPinnacle46715.22%11,176,000
Paula M Rhodes/Lori Harris/Brian D EvjenNorstar20630.00%8,049,398
James R Hoover/Stephen A FrickVestcor27622.22%8,860,852
Elizabeth WongAtlantic Pacific23521.74%10,467,111
Kimberly MurphyRoyal American27414.81%5,402,091
Jonathan L WolfWendover32412.50%5,839,370
Oscar A SolGreen Mills10330.00%4,275,000
Alberto Milo JrRelated30310.00%3,848,855

Top 10 – Total Amount of Preliminary Awards (annual tax credit requested)

Contact PersonDeveloper(s)Number of AppsPreliminary AwardsPercentageTotal Request ($)
Matthew RiegerHTG73810.96%13,358,687
David O DeutchPinnacle46715.22%11,176,000
Elizabeth WongAtlantic Pacific23521.74%10,467,111
James R Hoover/Stephen A FrickVestcor27622.22%8,860,852
Brianne E HeffnerSouthport47817.02%8,156,000
Paula M Rhodes/Lori Harris/Brian D EvjenNorstar20630.00%8,049,398
Jonathan L WolfWendover32412.50%5,839,370
Kimberly MurphyRoyal American27414.81%5,402,091
Robert G HoskinsNurock5240.00%4,671,000
Oscar A SolGreen Mills10330.00%4,275,000

A quick summary of applications reveals a few important pieces of information.

First, there are very few active organizations consistently pursuing tax-credit financing in Florida. Only 37 organizations submitted 4 or more tax credit applications during 2014, 2015, and 2016. Of those, only 28 won a single preliminary award. Among those 28 organizations, the median developer submitted 13 applications over the three-year period. Those applications yielded the median developer 2 preliminary awards; a success rate of 15.4%.

Second, those few active professional developers utilize very different strategies. HTG submitted 26 applications (55%) more than Southport, but ended up with the same number of preliminary awards. However, HTG’s deals are much larger, and the total amount of its preliminary awards is 63.7% larger than that of Southport. (I’ll evaluate the geographic dispersion of applications and wins another time. Presumably there is a relationship between the firms’ performance and the areas where they operate). HTG stands apart in its successful use of what I’ll call a “brute force” strategy. Although all of the top ten developers – by any of the measures above – submit many more applications than their competitors, HTG submitted 5x the median number of applications. Although the company’s success rate is well below median, the sheer volume of applications yielded a full pipeline of deals (without even counting any 4%/SAIL awards). Conversely, some organizations, especially those which frequently partner with PHAs, have success rates 2-3x that of the median competitor.  Norstar won 6 preliminary awards from just 20 applications. Roughly half of the regular competitors submitted around 10 applications during the three-year period, winning 1 or 2 preliminary awards. Again, an important caveat is that the protest process can lead to additional funded deals. Therefore, this quick evaluation does not reflect each and every 9% tax credit award in Florida.

Third, some developers are more successful than others. Given the role of the “lottery” as a tie-breaker, as well as the impact of the county award tally, it may be more accurate to say that some developers are luckier than others. Either way, some developers persevere in the face of loss over and over. One developer submitted 30 applications during the three-year period without winning a single preliminary award. That means someone spent $90,000 in application fees, did not win, and continues to compete for tax credit funding. Conversely, Atlantic Pacific submitted just 23 applications, which yielded more than $100,000,000 in tax credit equity.

Thank you for reading. Feel free to check out the data I used. If you see any errors, please leave a comment below.