Data and decontrol : a civic-tech approach for identification of predatory landlords in the New York City rent-regulated housing market

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Data and Decontrol:

A civic-tech approach for identification of predatory landlords in the New York City rent-regulated housing market

By

Meagan Cherita Patrick

Submitted to the Department of Urban Studies and Planning In partial fulfillment of the requirements for the degree of

Masters in City Planning at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2019

The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any

medium now known or hereafter created.

Signature of

Author...

ig

natu re red acted

...

Department of Urban StucLVs and Planning December 18th, 2018

Cetfidby....Signature

redacted'

C ertified by ... ...

Joseph Ferreira, Jr.,

Professor of Urban Pla riing and Operations Research Thesis Advisor

Signature redacted

A ccepted by ... ... P Christopher Zegras

Associate Professor of Transpo on and Urban Planning Chair, MCP Committee MASSACHUSET NTTUTE OF TECHNOLOGY

FEB

5 2019

LIBRARIES

[]

twice the share a decade ago."

The impact of concentration of rental units in the hands of corporate ownership has coincided with an increase in rent and an increase in inequality, as studies have shown: "Between 2000 and 2010, inflation-adjusted median asking rent increased between 21% (in the Midwest) and 37% (in the Northeast). During the same period, inflation-adjusted median incomes rose by

roughly 7% for households headed by someone with a high school education or less" (Desmond and Kimbro, 2015; see also Collinson, 2011) (Desmond, 2018). Researchers "have suggested that

amateur landlords typically adjust rent by considering their own moral and personal considerations of their tenants, while professional landlords often set rents after conducting sophisticated market analyses and coordinating with each other. And indeed, they found rents to be higher in communities in which rental property ownership was highly

concentrated" (Desmond, 2018). This concentration of ownership has been termed the financialization of the housing market, a phenomenon identified in the post-crisis landscape

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that involved a reshuffling of capital from other markets: aimed at increasing the liquidity of the real estate market.

On the national level, this translated into what has been termed "predatory equity": defined as "national investment firms buying bulk foreclosures and selling on land contract", creating "post-crisis housing markets [] marked by increasingly tenuous claims on property that are bound to the real and present threat of state-enforced eviction,

displacement, relocation, and the s huffling of ownership" (Akers, 2018).

The purchase of rent-regulated buildings has contributed to the rise of this phenomenon in New York City. These buildings had traditionally been

undesirable to buy as the cost to maintain them often just matched or outpaced the generated rent, and thus sold for less than a comparable

market-rate building. Reports have shown that the impact on those rent stabilized units has been a rapid de-crease in stock, and that there is financial incentive for this pattern to continue.

Bloomberg Business cites the pur-chase of Stuyvesant Town/Peter Coo per Village by Tishman Speyer and Black-Rock in 2006 as the beginning of corpo-rate interest in decontrol of

rent-regulated housing in New York City. "[By] attracting outside investors by promising that within five years the property's rental income would triple and its value would balloon to $7 billion. Their logic was simple: At the time of purchase, 73 per-cent of the complex's units were rent-stabilized. All they needed to do was destabilize them" (Van Zuylen-Wood 2016).

Stuyvesant Town/Peter Cooper Village, originally built by Metropolitan

Life for veterans and long a haven in New York City for the middle class, was home to about 20,000 people in 11,200 units spread across 110 buildings. At the time of sale, there were 8,037 rent stabilized

properties. After buying the property, Tishman Speyer hired three law firms and a licensed private investigator to try to remove rent-stabilized tenants. In the first year, they denied lease renewals to

800 rent-stabilized apartments on claims

that they were primarily living elsewhere. Tenants had to hire lawyers, with legal fees of several thousand dollars. Several tenants reported feeling harassed, including a 62-year old woman who was accused of having a primary residence in Illinois on the basis of another individual with the same name who did live there, unrelated to her. Of these 800 cases, approximately 40% were dropped and approximately 30% resulted in tenants moving out. By 2008, 740 units had been removed from rent regulation in the complex, a loss of a little less than 10% (Fernandez, 2008).

While this may have marked, for many, the beginning of predatory equi-ty in New York's rent-regulated market,

it hasn't come to represent it. Having paid $5.4 billion during the height of the housing bubble, the buildings became devalued and they were not able to decontrol the apartments at a rate fast enough to raise net income (Bagli, 2010).

In 2009, the New York Court of Appeals ruled that ownership had "improperly deregulated and raised rents on about 4,400 of the apartments while getting special tax breaks from the city" (Bagli, 2010).

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In 2009, the New York Court of Appeals ruled that ownership had "improperly deregulated and raised rents on about 4,400 of the apartments while getting special tax breaks from the city" (Bagli,

2010). In 2010, they fell into default on

the property, and shortly afterwards sold it.

Reports show that following pred-atory equity deals in rent-regulated

buildings targeted smaller developments, with more aggressive tactics to remove tenants, with ownership obscured by

LLC's, and with real estate management companies stepping in as middlemen. While there is an abundance of

qualitative analysis of these phenomena, quantitative analysis of the big data has been hampered by a lack of available data. While New York City laws require data to be available, rent stabilization is controlled by the state, which does not release information on rent stabilized units. Attempts by data activists to "liberate" this data and identify bad actors has been hampered by the

obfuscation of ownership relations by the usage of Limited Liability entities created for each building owned.

This work seeks to complement existing social and legal literature on the role of private equity-backed landlords in accelerating the loss of rent stabilized units from the NYC housing market, exploring the role of civic tech as a form of participatory governance.

Using data obtained by web scraping of publicly available tax documents, the author proposes a methodology to match rent stabilization changes to ownership with the creation of a relational database that spatially and relationally matches tax mailing address-es using a look-up table. This database has the potential to overcome

pre-existing data analysis of rent

regulation, identified by the author as:

1) the difficulty in tracking rent stabilized

apartments removed from the market given that the New York State

Department Division releases rent

stabilization data by building rather than unit; and 2) that civic tech attempts which have used tax ID data to create such a database have been limited in their ability to identify systemic abuse since financial interests are often obscured by the use of LLCs.

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CHAPTER TWO: LITERATURE REVIEW

"Networks and technology have

immense power to change the way that government is practiced. We can help lead into this world where governments are open by default, and citizens have much more opportunities to participate beyond voting, signing petitions, and protesting. They can actually reach into areas and build parts of government to address problems that citizens could

never touch before."

- Catherine Bracy, Director of Communi-ty Organizing at Code for America.

Cities are increasingly deploying 'big data' methods to engage citizens and receive feedback on policy change. With exponential growth in information processing power, there is an

unprecedented opportunity for decentralization of governance

accountability where, given access to open data, ordinary citizens can interrogate the effectiveness of

government initiatives. This is especially true in New York City, which was a

pioneer in providing access to open data with Local Law 11 of 2012, signed into law by the then-current mayor Michael Bloomberg. Local Law 11 amended the City administrative code to mandate that all public data be accessible on a single portal by 2018. It is perhaps surprising, then, that there hasn't been much quantitative analysis on fraudulent deregulation of the rent-regulated housing market, particularly given the amount of resources and workforce that has been devoted to the issue. The problem is that rent regulation isn't con-trolled by New York City.

It's controlled by the state, and the only data they provide is a list of

buildings which have at least one unit that's rent-stabilized. The only other data that is released to the City or its citizens, is a Census survey administered once every three years containing a total count of rent-stabilized and rent-control units for each of the boroughs.

The Basics of Rent Regulation The laws which govern rent regulation in New York are highly

complex, but units can be defined in one of two general categories: rent control and rent stabilization. In general, rent regulated units are located in buildings which contain six or more units, were built before 1974, and are not co-ops or condos. However, "There are many exceptions to these rules. (For instance, if you moved into the apartment BEFORE the building was converted to a co-op, the apartment may be stabilized. Also, some newly constructed buildings may be stabilized due to a 421-a or J-51 tax exemption even if the rent is above [set limits]" (Rent Guidelines Board, 2017).

In general, the apartment will continue to be rent-regulated for the incoming tenant if the vacancy rent is below the level set for the time period:

- Between 1993 - Between June $2,500 - Between June $2,700 - December 31, and June 23, 2011: $2,000 24, 2011 and June 14, 2015: 15, 2015 and Dec. 31, 2017: 2017 - present: $2,733.75 8

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Rent Control

Rent control applies to tenants, or qualified family members of tenants continuously living in an apartment since July 1, 1971, only in buildings built prior to 1947.

It began after the legislation in 1943 as part of the "Emergency Price Control Act" (EPCA)" signed into law by President Franklin D. Roosevelt as part of a federal response to "inflationary

pressures resulting from a fully employed wartime economy that channeled

resources exclusively to the war effort"

(NYS DHCR, "Rent Regulation"). On

November 1st, 1943, the Federal Office of Price Administration issued regulations freezing New York City rents at March 1,

1943 levels. The EPCA expired after the war on June 30, 1947, and was

subsequently replaced by the "Federal Housing and Rent Act of 1947," effective July 1st, stating that "new construction after February 1, 1947 was totally ex-empted from controls while pre-1947 buildings remained subject to continuing regulation" (NYDHCR, "Rent

Regulation").

The landlord may only increase the rent in two ways in a rent-controlled apartment: by Major Capital

Improvements, or by approval for an increase in the Maximum Base Rent (MBR), "granted only if the owner files for it [to the NYS Division of Housing &

Community Renewal (DHCR)] six months prior to the [two-year] cycle and certifies that essential services are being

maintained and that 100% of rent impairing violations and 80% of all over violations that were in place on January 1 of that

year have been removed" (MET Council on Housing). Furthermore, "[t]he landlord is only allowed to increase the rent for the tenants in the building if he receives

an Order of Eligibility and sends each rent-controlled tenant an RN-26 form. The rent the tenant actually pays, which

is called the Maximum Collectible Rent or MCR, is increased by 7.5% per year until it reaches the MBR.... Once the order is received, the landlord must mail out the RN-26 forms to each rent-controlled tenant for the 7.5% in the MCR within 60

days. If the RN-26 is mailed after the 60 days, the increase is prospective only.... Some landlords choose not to participate in the MBR system. And some landlords have so many violations, they are not eligible for MBR increases. For tenants in these buildings, the landlord is not permitted to collect any increase" (MET Council on Housing).

The end result is that the rent in rent controlled apartments tends not to increase until the rent control tenant

moves out. As a result of the 1971 Va-cancy Decontrol Law & 1974 Emergency Tenant Protection Act ETPA, rent-con-trolled apartments enter the rent stabili-zation market once they become vacant.

Rent Stabilization

In addition to decontrolled rent control apartments, rent stabilization can be found in buildings of six or more units built after 1947 but prior to 1974, as well as more recent buildings which left the Mitchell Lama Housing Program, Section

8, or which were financed in the context

of certain tax incentive programs.

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The rent stabilized sector began in 1969 "as a less stringent alternative to the controls already in place," with the establishment of the Rent Guidelines Board (RGB), a nine-member board of landlords and tenants. RGB regulates the stabilized sector and determines rent increases (Nagy 1995).

Rent in these apartments increas-es at the rate set by the Rent Guidelinincreas-es Board, which currently is set between 0-2% each year, unless they become vacant, upon which they are eligible for a much larger increase. "The 1974 EPTA regulation permitted a substantial rent increase after a tenant vacated... The RGB then allows minimal rent

increases until the apartment becomes vacant again" (Nagy 1995).

Abuse of the System

The literature contains various reports of landlords who employ abusive tactics to motivate vacancy of rent-controlled and rent-regulated

apartments: "Landlords ignore the duty to make repairs, illegally threaten to evict tenants, and refuse to cash rent checks to pressure tenants into

abandoning rent-regulated units. Consequently, landlords' buyout of-fers, which are often coupled with other means of "intimidat[ing] or pressur[ing] longtime residents to leave" including such abusive tactics as "leverag[ing] a tenant's immigration status to increase the pressure to accept a buyout" (Fisher 2015). Once tenants leave, rent is auto-matically increased 20% due to

apartment vacancy, incentivizing tenant turnover which can raise the rent to the levels necessary to achieve decontrol. 10

Literature has indicated that the landlord has further incentive to commit fraud by performing unnecessary improvements to the apartment or reporting inflated costs of those improvements. Known as indi-vidual apartment improvements ("AI"), the incentive was "originally meant to promote building renovations by allow-ing landlords to recoup

investments made to improve properties. However, due to a lack of government supervision, IAI are frequently exploited by property owners. "Unlike with an MCI [Major Capital Improvement], a landlord does not have to seek prior approval for IAI. Landlords of buildings with more than thirty-five apartments may collect a permanent monthly rent increase equal to one-sixtieth of the cost of the

improvement to the apartment. For buildings with thirty-five apartments or fewer, the owner can collect an increase equal to one-fortieth of the total

improvement cost. If the apartment has a tenant then the landlord must get

written consent for the rent increase from the tenant. No consent is required for IAI performed in vacant apartments. There is also no requirement for a landlord to prove the cost was reasonable, only that the money was actually spent" (La Mort). This, along with other loopholes, has meant that the rent regulated market is easily exploitable.

Effect of Financialization of the Market Given the capital needed for

improvements that can significantly increase the legal rent that a landlord can charge, the opportunity to commit fraud increases with the influx of capital

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through financialization of rent-stabilized housing by private equity funds. Funds "began to aggressively target this housing stock around 2005 and bought up 100,000 units (about 10% of the supply of rent-stabilized housing) by

2009 (ANHD, 2009b)... Advocates

termed the investments "predatory equity" to highlight the actors involved and the extractive nature of the

investments vis-'a-vis the supply of affordable rental housing" (Fields 2010).

"The profit expectations and debt load associated with predatory equity deals were predicated on rates of tenant turnover in the range of 20% or more a year, whereas the typical turnover rate for rent-stabilized units is 5%-10% a year (Rent Guidelines Board, 2009).

Meeting these turnover objectives required efforts to "promote attrition," which entailed systematic harassment such as building-wide eviction notices, baseless lawsuits for unpaid rent, aggressive buy-out offers, refusal to make repairs inside units, and threats to call immigration authorities" (Fields

2015).

Financialization has also meant that firms are motivated to obscure ownership, so that they do not meet the fate that Tishman Speyer did with

Stuyvesant Town, in which they were mired in litigation and eventually forced to pay restitution for rent overcharges by New York's highest court, the Court of Appeals.

Attempts to Combat Fraud

In a city where median apartment rents rose 75% between 2000 and 2012 ("The Growing Gap", 2014), replaced,

and with "the rent regulated housing stock [] disappearing at a rate faster than it is being replaced," a 2014 report by the New York City Comptroller recom-mended specifically action must be taken against fraudulent deregulation by land-lords. Specifically, the report cited a need to focus on "developments with large rent stabilized populations where there is evidence that owners employed legal and illegal tactics to expedite rent stabi-lized tenant turnover in order to remove those units from the regulatory system" ("The Growing Gap", 2014). The need for rent stabilization in New York City has been compounded by several factors in a city where home ownership is out of reach for the majority of the population.

Factors which have affected the supply of affordable housing are (1) tax benefits for homeowners, which "since 1980, housing related tax expenditures have far out-paced those for housing assistance," (2) the federal move away from public

hous-ing with a decrease in inventory of 18% from 1991-2007 and (3) a decrease in the

HUD budget of 66% between 1977 and 2008 (Desmond, 2018).

In a recent audit, City Comptrol-ler Scott Stringer found that "fraudulent activity may have expedited the loss of an untold number of rent-stabilized units from the City's regulatory system" (LaM-ort 2015).

In attempting to identify abuse of the rent stabilized market, civic actors

have stepped in. In justifying the devel-opment of his tool mapping rent

stabi-lization data, open data activist Krauss writes, "The secrecy blanketing the

stabi-lization program makes it very difficult to understand how loopholes in the

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program affect affordability in different neighborhoods over time. Not only does this make life hard for tenant advocates, but it provides cover for landlords who fail to tell the state (register) their stabi-lized apartments. Registration is voluntary

- another loophole in the law - and failure to do so could be an indication that they are overcharging their tenants" (Krauss 2015).

These landlords have also been prosucted under the city and state laws, although the State does necessitate a high burden of proof that may be hard to meet with just one building. The

"New York State Consumer Protection Law: Section 349 of the General Busi-ness Law" "prescription on deceptive practices generally applies to residential landlord-tenant relationships. However, tenants' actions under 349 may face a significant hurdle at the threshold: in order to state a claim under 349, the plaintiff must establish that the allegedly deceptive practice is

"consumer-oriented." To satisfy this threshold re-quirement, the plaintiff must show that the defendants' "acts or practices have a broad[] impact on consumers at large.... Once the plaintiff has established at the threshold that the defendant's conduct is

"consumer oriented," she must next prove a prima facie case by showing (1) that the defendant is "engaging in an act or practice that is deceptive or

mislead-ing in a material way"; and (2) that the plaintiff has been injured by the defen-dant's act or practice. Courts have adopt-ed an "objective definition" for the first element of the prima facie case: a defen-dant's conduct is "deceptive or

misleading in a material way" if it is

"likely to mislead a reasonable consumer acting reasonably under the circumstanc-es" (Fisher 2015).

12

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-CHAPTER THREE: DATA AND METHODOLOGY

The approach taken here can be suc-cinctly described as:

1. Identify what constitutes an abnormal

change

2. Cross-reference that change with AC-RIS sale date

3. Investigate "suspect" cases, develop SQL to identify their other properties,

and map those properties.

This approach is dependent on a

database structure created by the author

(pg. 16-17). 3.1 The Data

The data has been pulled from New York City's ACRIS database as well as data compiled by civic tech activists from OCR-read tax documents attached to every building on the DHCR list, which includes at least one building.

3.1.1 Data from ACRIS

ACRIS ("The Automated City Register Information System) is a database which allows individuals to search

property records in New York City from

1966 onwards. In this case, only the "Real

Property" data was used, which includes "Deeds and Other Conveyances,

Mortgages and Instruments and other Document" class in ACRIS. These documents typically impact rights to

real property and as such follow the real property rather than the individual (DoF,

2015). Each document is assigned a

Document ID, which serves as the unique primary key in the table Real Property Master for every one of these documents

Each document ID is associated with a tax lot through the identification

"borough", "block" and "lot", and there are generally many document IDs for each tax lot, as each tax document is filed. The data from that document is organized into five data tables: 1) Real

Property Master; 2) Real Property Legals;

3) Real Property Parties; 4) Real Property

References; and 5) Real Property Remarks. (see Appendix for data dictionaries).

While there is only one unique entry for "documentid" in the "Real Property Master", there may be multiples of the documentid in other tables which connect back to the Master table as a foreign key. This data was imported into the larger database using the scripts provided on the following github: https:// github.com/fitnr/acris-down load.

3.1.1.0 Author Changes

In order to link this table to the "Tax Summary" table, a new column was created called "bbl" which is not in the original. It is a concatenation of

preexisting fields "Borough", "Block" and "Lot" in the Legal table, and acts as a foreign key to the primary "bbl" key in the Tax Summary table. The original columns are not altered.

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CHAPTER THREE: DATA AND METHODOLOGY

3.1.2 Civic Tech Tax Data (Tax Summary) The ACRIS data is connected via foreign key (bbl) in the Real Property Legals to the primary key (bbl) in the "Tax

Summary" table, where bbl represents the "Borough/Block/Lot" number. While there are likely to be many entries in the

Real Property Legals table for each document associated with that tax lot, there is only one entry in the Tax

Summary table for each tax lot (identified by bbl). It is then connected to the third piece of the database (Compiled Data), a series of mostly look-up tables.

3.1.2.0 Author Changes

This table has a "suspect" column

added to it, which is a Boolean true/false corresponding to whether the change in any of the years is greater than 10% (see methodology), for lots with more than 10 apartments within two years of a sale date.

This TaxlD table was taken from data uploaded here: http://taxbills.nyc/. It was created using the list of buildings that

DHCR supplies as having at least one unit which is rent stabilized, the data was collected as part of a Civic Tech effort by multiple activists using tax data led by John Krauss from 2007-2016. As Krauss writes, he built the site together with the "help from a few civic hackers" as "a collection of every tax bill going back to 2008 for every building that might be stabilized in New York City" (Krauss, 2015). This involved "downloading hundreds of thousands of tax bills PDFs over several months," done "because New York City's Department of Finance (DoF) wanted $50,000 to mail [a files that could already be found free online" (Krauss, 2015).

Krauss' Map from Data

(Krauss, 2016)

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The DoF had quoted him that the work would take approximately 1,0000 hours at $50.36 per hour (Krauss, 2015). As displayed in the below map, Krauss classifies tax lots into four categories of evolution from 2007-2016: ones in which the number of rent stabilized units 1)

"experienced major losses", 2) decreased, 3) remained steady, and 4) increased. He attributes the number of increases primarily to "new devel-opments with affordable housing"; the author would argue that this reasoning discounts the 20,000 units during that time period exiting the rent control

market and entering the rent stabilization market. The only significant contributions were in the first two years, with an

addition of 4,542 units out of 892,431 units listed as rent stabilized that year, about .5% of that year's stock.

While Krauss looks at the data as a rate of change accumulated between

2007 and 2016, the author argues in

favor of a focus on annual shifts in order to accommodate for increases due to apartments leaving rent control, and to

relate these changes to changes in ownership. The gains into the rent stabilization market which represent departures from the rent-control market are arguably more important than loss of

rent stabilized units. Rent control

tenants tend to be the elderly. given that to maintain the status they must have

lived in the apartment since 1971, or successfully succeed ownership as a live-in family member.

While Krauss makes the summa-ry data available in CSV format, he also has available the raw data, available in an unstructured "json" format which the author

was able to navigate using a

programming language known as Mon-goDB. Given that the mailing addresses were tagged when read from the PDFs, the author was then able to extract them and pull them back into a relational data-base so that it could be queried with SQL commands. This was done in the

"Author-Compiled Data" to combat is-sues that listing of ownership, particularly where tax lots are owned by "LLC's." The billing address is not listed for

owners in Krauss' CSV tables, as each tax lot is tagged with the current owner, with name pulled from what is listed in New York City's planning database (PLUTO).

By pulling from the raw data, we are able

to see the billing address for the owner for every tax document they filed, which even in the case where there is an LLC, often contains the corporation's name.

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3.2.1 Data Summary

The assembled tax data which was used was from 2007-2016, with a total number of tax lots which at any point had rent-stabilized apartments totaling 45,685. Of these, only 30,064 had data for every year.

For each tax lot, Krauss has added in data from PLUTO from the most

current year detailing the characteristic of the lots. 4000-3000 2000- 1000-0-

0-There is a mean of 30.62 residential units, 31.43 total units, 4.9 total floors, and

1.27 buildings, with the mean year built in 1916. There is a significant drop in the number of tax lots with registered units between 2008 and 2009. While there are some tax lots that show a decrease to 2018 which could represent a lack of rent-stabilized units after that year, there are 723 tax lots alone which stop being registered after 2008, representing 1.7% of all tax lots in 2008.

200 460

Residential Units per Tax Lot

600 0 200 400

Total Units per Tax Lot

750-

25C-

0-1900 1950

Avg. Year Built

2000

I

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Total Number of Floors per Tax Lot

0 do 2000- i00 0-1;50 18 50

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3.3.1 Examining complete cases and changes in the tax data:

We can see that there are a significant number of tax lots which stopped registering their rent-stabilized numbers after 2008, and so to be able to look at fluctuations, only complete cases were considered. The number of

complete cases (tax lots with rent sta-bilized units reported for every year) is 30,064, which represents a removal of 15,621 tax lots from the total dataset of 45,685, or about 1/3 of the total.

Tax Lots with Entries for Rent-Stabilzed Units per Year

-Removing all lots which report a greater number of rent stabilized units than their 2017 PLUTO units, we are left a much larger reduction: 17,555 tax lots, a significant loss of 12,504. -40,000 39,000 a 38,000-2008 -2010 2012 2014 2016 41,000 19

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Rent Stabilized Units in 1st Year

-

Rent Stabilized Units in 2nd Year

Number of Total Units on the Lots

Changes in the number of rent

stabilized units was calculated using the

above formula. This formula was

normalized over the total number of

units. This is because the data only takes

into account rent stabilized units and

not rent control units. As such, positive

changes could be seen when a unit

leaves rent control and enters rent

stabilization, with positive changes

having potential implications for an

overall loss of rent regulation given that

they are now removed from the more

stringent rent control group.

We have removed buildings with

less than 10 units as a change of 10% in a

ten-unit lot would be the removal of one

unit, which could be insignificant.

Pulling this into R, we can plot

the histograms to get an image of the

change over the nine year span (with

limits ranging from -50% change to 50%

change, and 10 bins).

We also see consistent patterns of

no change when we look at histograms

of change in lots with greater than ten

units.

This change also stays consistent

when you only look at lots with less than

10 units, as you can see here in the plot

of change in rent stabilized units

reported between 2007 and 2008

(histo-grams plotted on opposite page).

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sChanges nRet C bizedUni 0 2007-200 0 2000-2010 900 0- 9000- 6000.--03000 - 0- 9000-0 300 0S900 - 0S3000 - 0-10 20 10 20 10 -20 -0 -20 10 20 10 -20 -10 -i0 -40 -20 -10 0 10 2015-2016 20 20 20 9000 9000-* 60m- - 6000 -8 3000. 8 3000-0- ] - - --10 -5 0 5 10 -10 -0 0 2007-2008 2006-20 -to -10 -5 -5 -10 0 2009-2010 2011-2012 -5 20 2013-2014

ifi

-to -5 5 5 5 10 10 10

9000-I

60009000 -6 300 0-9 000-8 3000-0. 0-5 0Jg -10 -5 0 5 2010-2011 -10 -10 -5 -5 0 2012-2013 0 2014-2015 6 5 5 10 I 0 2011-2012 0 10 2013-2014 10 20 60003000 - 09 000 -U 300 0. 9000 - 0w- 0--20 -10 20 10 -20 -10 20 -0; 0 2005-200 0 2010-2011 0 2012-2013 0 2014-2015 10 ;o 20 20 o0 20 9000- 0m- 9000-8 3000-0~. 0- 9000- 0m- 900-:: 6000 - 8300 0-10 10 ;o 2015-2016 1o - 1--10 -S

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3.3 Lining Up Abnormal Changes with Sale Dates

Given the previous findings, it was proposed to look at the abnormal

changes of greater than 10% in buildings which had an ownership change recorded within a year in either direction. Buildings with an ownership change were taken from ACRIS records which had an entry for "percent

transferred" not equal to 0, as this

proved more effective than pulling those marked DEED or Property transfer codes. This results in a number of suspect tax

lots per year which is higher in 2008-2012, a spike which corresponds with the

narrative of predatory landlord

activity which began in 2006, accessing a rent-regulated housing stock that was previously untapped.

Combined throughout the years, this gives us a sum of 2,774 unique tax lots that are suspect. This represents

12.6% of the 21,903 tax lots which have more than ten units where the total

number of units is not reported to be less than the rent stabilized number of units for either year 1 or year 2.

Mapping the suspect cases (pages 24-25) visually suggests pattern clustering in geographic areas which would present a larger profit margin: in areas of Manhattan, around universities such as Fordham in the Bronx, around subway lines, and in the gentrified areas of Brooklyn above Prospect Park (map on following pages, pp. 24-25)

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Number of Suspect Tax Lots by Year

700

600 500 400 300 200 100 0

2007-

2008-

2009-

2010-

2011-

2012-

2013-

2014-

2015-2008

2009

2010

2011

2012

2013

2014

2015

2016

23

(24)

CHAPTER THREE: DATA AND METHODOLOGY N -A- a A;" ee **er*

-

a-lb* 9 . 9S g~ 24 A:p

(25)

*04

Ago

Suspect Cases

Tax Lots with greater than 10 units

and greater than annual 10%

change in rent stabilized units

within one year of sale date

Legend

+

Subway

m

Parks

e

Suspect

-

All Rent

OSM Base M

Tax Lots

Regulated Lots

25 -awl

(26)

CHAPTER THREE: DATA AND METHODOLOGY 2007-2008 2012-2013 2009-2010 26 2013-2014

(27)

7 k

I

xi -77 -41 lit 2011-2012 2014-2015 2015-2016 27 2010-2011 -1 e a

(28)

3.4 Mapping Ownership

Ownership corresponding to the suspect tax lots can then be mapped using the address information in the "Parties" table of the ACRIS dataset which is recorded at the time of sale, supplementing with information from the raw tax data pulled into a database through OCR by Krauss and his team.

This methodology takes the approach proposed by J. Ferreira of a lookup table as an alternative to top-down or bottom-up strategies for cat-egorizing ownership of parcel data. "Instead of correcting spelling errors in our copy of the official parcel data, we can create a new lookup table that lists each of our corrections next to its original

name" (Ferreira, 1996). This approach was taken not only due to the large number of errors that may be in user-reported data but also the inconsistent way in which names or addresses are reported. This approach also allows the corrections to be isolated from the original data so that the analysis can be more easily redone as additional

corrections are found and accumulated.

(29)

CHAPTER FOUR: CASE STUDY

g~ 4

&

/

4.1 Area of the Case Study

This case study uses a small section of the city in one of the areas where fraud is

predicted to occur, in the 209.01 census tract in West/Central Harlem centered

around 125th street and near Columbia University. There are 24 lots which had rent-stabilized units over the period from 2007-2016, although only 19 of them have an entry for rent stabilized units for each year.

4.2.a Data Cleaning

In this microsample, we have 24 tax lots which have listed buildings as having rent stabilized units, although only 19 of them have data for every year. Using the methodology listed above, we already have marked all suspect lots.

Having removed incomplete data, data where the residential units is

reported to be less than the rent sta-bilized units, buildings with fewer than

10 units, and data where percentage

changes is less than 5%, we are left with four lots which have been marked as

"suspect." The data is displayed on the following page with the "suspect" cases

highlighted on the following page, pp. 30.

(30)

Rent Stabilized Units Reported for Tax Year

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

bbI unitsres unitstotal yearbuilt

8 14 14 14 14 14 14 1 8 14 1 14 1 8 8 8 14 14 14 65 1019500062 11 11 11 11 10 10 10 10 10 10 10 10 20 28 21 10 6 6 53 24 8 16 23 12 20 19 16 40 23 10 10 20 28 21 10 6 6 53 24 8 16 23 12 20 19 16 40 23 10 10 20 28 21 10 6 6 53 25 8 16 22 12 20 19 17 40 14 7 10 31 28 21 10 6 6 53 25 8 16 22 12 20 19 17 40 23 6 10 30 23 21 10 6 53 25 8 10 28 21 21 10 6 53 25 8 16 16 22 12 12 20 20 19 17 41 23 3 19 17 40 23 I 10 28 20 21 10 6 53 25 8 16 21 12 20 19 17 40 12 1 10 28 g0 21 10 6 6 53 25 8 16 22 12 20 19 17 41 It 12 10 3 28 21 10 6 6 53 25 8 16 22 31 40 19 17 40 i2 12 8 1901 8 1901 14 1900 65 1901 10 1901 10 44 10 23 10 7 7 53 31 9 19 24 33 40 21 19 46

w

m

1901 1926 1920 1997 1901 1905 1901 2006 1901 1910 1901 1901 1910 1901 1901 1901 1901 1921 1901

cases marked as suspect by algorithm are highlighted in yellow

30 1019500005 1019500006 1019500061 1019520051 1019500063 1029520011 1019660059 1019660049 1019500064 1019520001 1019520003 1019500057 1019660033 1019660036 1019660042 1019660047 1019660066 1019660102 1019660104 1019660046 1019660052 1019510014 019660039

(31)

4.2.b. Investigating lots identified as suspect:

Of these four, only three of them were listed as having a sale or a transfer:

The fourth lot represents a limitation of the methodology, as it doesn't list any documents to indicate sale or transfer, only mortgages and an "Assignment of Leases" (AL&R). bbl TN 1019520011 "1019660059 'I "1019510014 "o documentid 20101025001 71001 "2017061600 346001" "2011040700 353001" "2013061901 788001" "2012122500 047001" Doctype " d0filled "DEED" "2010-1144 "DEED" "RPTT&RET" (Real Property Transfer Tax) "RPTI" (Real Property Transfer Tax) "DEED" "2017-06-19 "2011-04-15 "2013-07-09 "2013-01-04'

I Tax Lots with Sales or

Transfer-datornodiffied "I "2010-11-04" "2017-06-19" " "2011-04-15" "2016-06-21" & "2013-10-25" " "2013-01-04"

4.2.b. Graphing the Cases:

sale date from ACRIS data 80 70 60 -50 ----2008 2010 - Percent of residential units rent stabilized from tax data

2012 2014 2016 31 "100" "100" "100" "74" "100n

(32)

property transfer date from ACRIS data 80 ---- _ 2008 2010 201 - Percent of residential units rent stabilized

from tax data

property transter date from ACRIS data I

i

2010 2012 2014 - Percent of residential units rent stabilized from tax data

2016 100 90 -70 -80 60 40 20 0 2008 32 2 2014 2016 I

(33)

4.2.d. Mapping Ownership

The traditional method of identifying own-ership in planning is either through the PLUTO Database, which is how it has been identified the "Tax Summary Dataset." We can, however, automatically pull ownership from ACRIS using queries which identify the Document ID for the sale of the suspect lots and using the document ID to query the ACRIS Real Properties Table. We can, however, automatically pull ownership from ACRIS using queries which identify the Document ID for the sale of the suspect lots and using the document ID to query the ACRIS Real Properties Table. However, the ACRIS data does not always have the owner listed, which is when we look at the mailing

Date Filed

2010-11-19

2011-02-18 2011-06-10

address during the period the property was owned by the LLC. In this case, for each of the properties it was relatively easy to find the owner name. In further cases, however, it may be necessary to identify ownership by the mailing address on its own.

Address Listed

i351 WEST 125 STREET\n

W51 WEST 125 STREET~n 351 WEST 125 STREET\n\n

2011-06-26 NEWCASTLE~n~nNEW YORK, NY 10016-8731 2011-11-18 NEWCASTLE~nnNEW YORK, NY 10016-8731

2012-02-24 351 WEST 125 LIMITED PARTNERSHIP nNEWCASTLE~n72 MADISON AVE. FL 6 2012-06-08 351 WEST 125 LIMITED PARTNERSHIP~nNEWCASTLE~n72 MADISON AVE. FL 6 2012-W-17 351 WEST 125 LIMITED PARTNERSHIP-nNEWCASTLEn72 MADISON AVE. FL 6 2012-11-30 351 WEST 125 UMITED PARTNERSHIP\nNEWCASTLE\n72 MADISON AVE. FL 6 2013-02-22 351 WEST 125 UMITED PARTNERSHIP\nNEWCASTLEr72 MADISON AVE. FL 6 2013-02-27 351 WEST 125 LIMITED PARTNERSHI N E\n72 MADISON AVE. FL 6 2013-0W-23 351 WEST 125 UMITED PARTNERSHIP\nN 72 MADISON AVE. FL 6

2013-11-22 351 WEST 125 uMITED PARTNERSHIP\nN 72 MADISON AVE. FL 6

2014-02-21 351 WEST 125 UMITED PARTNERSHIP\n 72 MADISON AVE. FL 6

2014-06-06 351 WEST 125 UMITED PARTNERSHIP\nN 72 MADISON AVE. FL 6 201408-22 WEST 125 ULMTED PARTNERSHIP\n20 7SON AVE. FLL9\nEW YORK W

2014-11-21 351 WEST 125 LIMITED PARTNERSHIP\n270 ISON AVE. FL 19\nNEW YORK, W

2015-02-20 2015-06-05

351 WEST 125 UMITED PARTNERSHIP\n270 M

351 WEST 125 LIMITED PARTNERSHIP i270 M

DISON AVE. FL 19\nNEW YORK, W OISON AVE. FL 19%nNEW YORK, W

4

-owner name ₃₃

k. :1

(34)

Using the

information from ACRIS and the mailing address field on all of the tax forms filed for the properties, we are able to find both the owner name and the address for that owner name for each of the cases and give them a firm key and an owner type ("S" for "suspect" and

"NS" for "not suspect."

oirmname ofrmadd Latfte Longitude ofirmkey otype

NEWCAST 270 MADISON AVE, NEW 40.7512W4 - 1 3

LE REALTY YORK, NY 10016-060d1 1 73.98071

14

ALPHA 2735 WEBSTER AVE, BRONX, 40.865259 - 2 IS

REALTY NY 100458 1 73.88744

36

HARRJOY 788 RIVERSIDE DR. APT 1 E, 40.834162 - 3 Ng

REALTY NEW YORK, NY 1 73.94750

88

MONA 111 GREAT NECK RD. STE 514, 40.785373 - 4 S

H REALTY GREAT NECK, NY 2 73.72948 95

EUAMAR 423 W. 125TH ST., NEW YORK, 40.812130 - 5 NS

REALTY 1 NY 8 73.95484

62

EUAMAR 101 W. 69TH ST. APT IC, NEW 40.775771 - 6

REALTY 2 YORK, NY 6 73.98036

29

Property Owners of Lots with Abnormal Change in Rent Stabilized Units in Census Tract 209.01

2.5 0 2.5' 5 10 Mi Lt-. ! flop' ~,' IA 4 4T Vw. ~RKALT/ Owner Addresses * Not Suspect " suspect

ED NYC Borough Boundaries

- Parks

OSM Standard

31.

(35)

4.2.d. Case Evaluation

Mapping ownership in the suspect tax lots in the Case Study shows a

geographic shift in ownership from less-empowered areas of the city like Harlem and the Bronx to wealthier areas such as midtown and the North Shore in

Long Island. Further investigation into each of the individual tax lots is

enlightening.

For lot 1, which is currently listed under an LLC, we see a large increase

in the number of rent stabilized units corresponding to the sale date which can be interpreted as a dramatic loss of rent control units which then enter the rent-stabilized market. After which, we see a decrease in rent stabilized units, which while comparatively gradual, is at a rate far outside the norm. Mapping the owner pre-and post-sale, we see "Alpha Realty Corporation" in the Bronx, as opposed to the LLC-listed "Newcastle Realty Corporation" in midtown. A quick search of Newcastle Realty shows mul-tiple cases against them for real estate fraud as a private-equity funded realty corporation run by Margaret Streick-er-Porres, a woman once named by the Village Voice as one of New York City's top slumlords. In a news article on land-lord-tenant tension, the New York Times reports that she's the daughter of John H. Streicker of Sentinel Real Estate Cor-poration, "a large real estate company that manages a portfolio of $5 billion in

assets for institutional investors, including

50,000 apartments) (Barbanel, 2006).

For tax lot 2, there is a precipitous drop in the rent stabilization count after a property transfer. Using the tax data pulled from the json file, the owner, ElJamar Realty, stays the same. However, mapping the address reported on the tax documents, it's clear that ownership is not the same: having moved from Harlem to the Upper West Side. While this does not appear to be a case of private-equity backed tenant harassment, it certainly appears to be worthy of further

investigation.

For tax lot 3, we also see a

precipitous drop off following the sale of the building to a firm that is listed as an

LLC, but whose tax documents show to

be Long Island-based Monarch

Realty. Monarch Realty was sued in 2013

by tenants of six buildings in Harlem

alleging harassment by "19 plaintiffs, mostly elderly people who lived in the building for decades," including "trying to evict them for trumped-up reasons, failing to make repairs, sending out rent bills with bogus charges tacked on, charging late fees when tenants refused to pay those charges, and demanding that tenants move" (Wishnia, 2013).

(36)

CHAPTER FIVE: SUMMARY & FINDINGS

While the preliminary results of the

Census tract may seem minor given that

it only uncovered a small number - only

three out of the 24 tax lots - the

information it gives us about which

landlords may be problematic is not

minor. We can actually use the raw tax

data to find other buildings owned by

the suspect owner. A search for

Newcastle returns 57 rent-stabilized tax

lots. Nineteen of these tax lots have

been classified as "suspect" using the

methodology's algorithm, an abnormally

high rate.

For tax lot 3, we also see a precipitous

drop off following the sale of the

building to a firm that is listed as an LLC,

but whose tax documents show to be

Long Island-based Monarch Realty.

Mon-arch Realty was sued in 2013 by tenants

of six buildings in Harlem alleging

ha-rassment by "19 plaintiffs, mostly

elder-ly people who lived in the building for

decades," including

Al

A

.11

L

~iiJ ~

4 t

Legend Subway M Parks

OSM Base Map

Newcastle Lots

" suspect

* Not Suspect/Missing Data

36

Rent-Stabilized Tax Lots Owned by Newcastle Realty

(37)

"trying to evict them for trumped-up reasons, failing to make repairs, sending out rent bills with bogus charges tacked on, charging late fees when tenants refused to pay those charges, and de-manding that tenants move" (Wishnia,

2013).

The case study also revealed some limitations of the methodology. One limitation is that using cases where "percent transferred" does not equal 0 does necessarily correspond to a sale date, and so we run into issues as seen in the fourth tax lot which was not featured, where it was discovered upon looking

at the ACRIS data that there was no sale date.

(38)

CHAPTER SIX: CONCLUSION & FURTHER STEPS

This thesis demonstrates that civic tech can overcome obstacles of

identifying ownership even when those owners have motivation to stay hidden, and it can be used to identify

problematic patterns of behavior even when the data isn't publicly available.

Even with tens of millions of records, the method of using data to collect candidates for investigation and then using the tax data to identify ownership is feasible. And the

methodology is expandable - there other sources which can give us candidate par-cels which would need investigation. The next logical expansion of the

database would be to add 311 building complaints and building violations as other methods of identifying suspect lots.

There is so much potential for analysis using this data. Perhaps the most important potential use for this data, however, for this research is to make a step forward towards overcoming obstacles created by financialization in the prosecution of fraud, as it can show the repeated pattern of behavior

necessary to meet the threshold for prosecution. With decentralized and parallel actors interpreting the data, we can combat the traditional obstacles posed by financiallization,that "the actors involved in predatory equity deals are difficult to target and hold accountable because they are unfamiliar, diffuse, and spatially removed." (Fields 2015).

While these obstacles are real, they are not insurmountable: this thesis shows it's also possible to use the aggre-gated tax data (the address listed for tax purposes for each lot during all years of ownership) to plot ownership.

In cases where, like with Kushner's

corporation, the address is home to

multiple real estate companies, we can

build those exclusions into the query:

select * FROM rentdata.taxid where

ucb-bi in (SELECT BBL FROM

rentdata.mail-ing where (value like '%KUSHNER

COM-PANIES%' OR VALUE LIKE '%KUSHNER

REALTY%' OR VALUE LIKE '%666 FIFTH AVE%' OR VALUE LIKE '%666 5TH AVE')

AND VALUE NOT LIKE '%ROCKROSE%' AND VALUE NOT LIKE '%APT. 15%' AND VALUE NOT LIKE '%5108%');

By identifying actors with suspect

levels of activity, we can continue to

create these specialized queries that can

identify buildings even when the

company name is left out of the tax

billing address. In addition to using data

to identify problematic tax lots which can

lead to the identification of problematic

landlords, it would also effective to use

community and literature input to find

names and addresses corresponding to

problematic landlords, and create

queries for the look-up table that can

map their properties. By mapping the

multiple properties of corporations who

have been identified as problematic, we

can provide prosecutors the necessary

evidence to show a pattern of behavior.

(39)

- Rent-Stabilized Tax Lots Owned

by Kushner

Legend

Subway / Parks

OSM Base Map

Tax Lots

SeSuspect

IA / / Not Suspect/Missing Data

(40)

SCALING THE METHODOLOGY

Ultimately, the goal would be to scale the methodology so that complicated

queries like the one cited for Kushner above would not be necessary, and pull-ing up parties involved in sales associat-ed with problematic patterns of behavior would automatically yield the target cor-poration rather than the shell corcor-poration listed for the tax lot.

Expanding on the lookup method-ology developed by Joe Ferreira Jr. described earlier in this piece, we can accumulate knowledge to connect the dots in ownership so that we can later reuse that knowledge for use simpler, more generalized queries to identify ownership. The most promising way to do this would be a series of targeted SQL queries focused on ACRIS parties

table, using information gleaned from the civic-tech scraped mailing

addresses on their tax filings. ACRIS provides a number of key advantages in identifying ownership over more

traditional forms of databases used in urban planning like PLUTO, primarily that it includes the address for the owner. However, it also may be superior given the chronological component of analysis of market transactions, with ownership tied to the date the tax document was filed for sale or purchase.

As an example of how this could be done, we can look at the example of Newcastle Realty, which is unsearchable in the ACRIS database in the name field but often featured by its mailing address, although its name can be found as a

"c/o" in the first address field. Doing a search where the names of the LLCs and Newcastle are grouped results, such as: 40

select name, address 1, address2, count(*) AS variances

from rentdata.real-propertyparties

where address 1 like '%NEWCASTLE REALTY%' group by address 1, address2, name

order by variances, name, address 1, address2;

This query returns 172 results, but many of these are duplicates due to slight dif-ferences in reporting, like:

name

addressl

101 WEST 78 CONDOMINIUM 101 WEST 78 CONDOMINIUM 101 WEST 78TH LLC 101 WEST 78TH LLC C/O NEWCASTLE REALTY SERVICES LLC C/O NEWCASTLE REALTY SERVICES LLC, C/O NEWCASTLE REALTY SERVICES C/O NEWCASTLE REALTY SERVICES, LLC address2 72 MADISON AVENUE 6TH FLOOR 72 MADISON AVENUE 6TH FLOOR 72 MADISON AVENUE, 6TH FLOOR 72 MADISON AVENUE, 6TH FLOOR

As you can see, the "name" is actually the name of the LLC, whereas the

"addressl" where the C/O is listed is the actual ownername. As such, in order to create a lookup table, we would have to take all three columns because they all

have the potential to contain actual owner information beyond the LLC.

We can take these unique instanc-es of entriinstanc-es to create owner lookup ta-bles, which contain, like in the case study,