foreclosure report

Foreclosure Crisis: Disproportionate Impact On

African-American and Latino Households and Neighborhoods

An Analysis of Newly Released Data

A report by New York Communities for Change 2-4 Nevins Street Brooklyn, NY 11217 (347) 410-6919 [email protected] www.nycommunities.org

Issued January 2011

Executive Summary Everyone knows there is a huge foreclosure crisis. Recently the country has seen banks forced to suspend foreclosures as more and more irresponsible, sloppy and potentially illegal practices come to light. This new analysis provides a closer look at New York City and Long Island to reveal the disproportionate depth of the impact on minority communities, where modifications have been scarce and widespread foreclosures threaten African-American and Latino neighborhoods. New data from 2010 shows that there is an indisputable connection between race and the likelihood of being served with pre-foreclosure notices. In New York City, Long Island and Westchester, there are thousands of African-American and Latino families and many neighborhoods that can be helped were banks to make more mortgage modifications. These minority neighborhoods are in crisis – a crisis created by both speculative home pricing, which allowed families to refinance their properties for more and more money while stripping out any equity, and by lenders who targeted these neighborhoods with predatory loans (including adjustable-rate mortgages, option arms, interest-only loans and loans with negative amortization). From 2004 to 2007, in both New York City and Long Island, lenders were four to five times more likely to issue high-cost loans to African-Americans and Latinos than they were to whites – and this fact held true for both purchase loans and refinance loans. Often these loans were specifically marketed to African-Americans, Latinos, low- and moderate-income homebuyers and seniors. In 2007, the mortgage market started to collapse, and as a result house values started declining. This left many homeowners underwater on their mortgages – that is, with mortgage balances higher than the current value of their homes. Being underwater prevents families from refinancing their homes. Additionally, many homebuyers are losing their jobs because of the 2008 financial collapse. African-Americans and Latinos have been hardest hit by job losses: between June 2009 and November 2010, 557,000 African-Americans and 292,000 Latinos lost their jobs nationally.1 New data analyzed for this study show that African-Americans and Latinos make up 32% of homeowners in New York City, but accounted for 56% of the December 2009 to December 2010 pre-foreclosure notices issued, making them 175% more likely than the general population of homeowners to be in foreclosure. Similarly, in Long Island’s Suffolk County, African-American and Latino homeowners are more than twice as likely as whites to be in foreclosure, as they constitute only 13% of homeowners but have received 30% of the pre-foreclosure notices issued. An aggressive program of mortgage modification is the absolutely most certain vehicle to stabilize these neighborhoods. The homeownership gains of African-Americans and Latinos need to be protected in the New York metropolitan area. The stability of these neighborhoods is at stake. (The cost of foreclosures to communities is discussed in the appendix of this report.) The banks and servicers need to come forward and quickly modify mortgages for people who are in danger of losing their

1 http://online.wsj.com/article/SB10001424052748703946504575470001733933356.html#articleTabs%3Dinteractive

homes, which is not currently happening. Informal discussions with loan counseling operations in New York City show that at most 16% of households that apply for permanent modifications get them. Additionally, when banks do modify loans, they must start reducing the principal balance. As recently stated in a report by Amherst® Securities Group, LP:

“We have repeatedly made the case that principal reduction is the least costly and only permanent solution for defaulted loans.”2

The third- and fourth-largest servicers of mortgages, JP Morgan Chase and CitiBank, are based right here in New York City and especially need to step forward, to lead the way, and to deal with this challenge. Overall banks and servicers need to:

• Offer homeowners who are underwater on their mortgages principal reduction instead of merely requiring all of the debt to be repaid at a later date (forbearance).

• Offer sustainable long-term loan modifications to homeowners with a permanent affordable interest rate. This sustainability includes lowering the interest rate, taking into account household debts and medical expenses, and it must be in place for the life of loan.

• Eliminate all fees that have accumulated as a result of being behind on the mortgage once a modification is requested.

2 Amherst Mortgage Insight, October 1, 2010, Page 13.!

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Introduction In the years leading up to the housing crisis, African-Americans and Latino-Americans had been making progress in achieving one of the big pieces of the American dream: owning a home. Now, in the face of predatory lending practices and declining home prices, those gains are slipping. In the country as a whole, African-Americans and Latinos are losing their homes at a faster rate than whites and are losing the ground they had gained in homeownership. This chart shows the national loses in homeownership levels by race over approximately the last three years3:

In downstate New York, there are thousands of African-American and Latino families who would benefit from mortgage modifications. These families cannot keep up with their current payments, and since they owe more than their homes are worth, they cannot simply refinance their mortgages; it is very likely that they will end up losing their homes without a mortgage modification. Banks must implement a quick and efficient plan to do the needed modifications. African-Americans and Latinos are disproportionately facing foreclosure. New York Communities for Change has analyzed information by zip code and county that shows a higher rate of foreclosure notices being issued to African-American and Latino homeowners than to homeowners as a whole from December 2009 to December 2010. (See the appendix for the information broken out by zip code.) 3 The source of the chart is the Center for Responsible Lending’s report called A National Tragedy: HMDA Data Highlight Homeownership Setbacks for African Americans and Latinos which can be found at: http://www.responsiblelending.org/mortgage-lending/research-analysis/HMDA-issue-brief-final.pdf

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In New York City, African-Americans and Latinos are 175% more likely than the general population of homeowners to have received a pre-foreclosure notice between December 2009 and December 2010. In Suffolk County, African-Americans and Latinos are 234% more likely than the general population of homeowners to have received a pre-foreclosure notice.4 Receiving a pre-foreclosure notice is an indicator of non payment. Upon receipt of such notice, many African-American and Latino homeowners have reached out to their banks for assistance in the form of a modification, and most have not gotten the modification. Mortgage modifications are necessary to:

1) restructure bad or predatory loans; 2) take into account the impact of the economic crisis on African-American and Latino incomes; and/or 3) account for the loss of value of the home (since this prohibits a straight out refinancing of the mortgage).5

Right now in New York City, African-Americans and Latinos are 165% more likely to be underwater on their mortgages than homeowners as a whole. In Suffolk, Nassau and Westchester counties, they are over 200% more likely to be underwater on their mortgages than the total population of homeowners. County/Area Percentage of

African-American and Latino Homeowners

Percentage of pre-foreclosure notices to African-American and Latino Homeowners (12/09 to 12/10)

Ratio of % African-American and Latino pre-foreclosure notices to African-American and Latino home-ownership

Percentage of homeowners under-water on their mortgages who areAfrican-Americanand Latino (December 2010)

Ratio of % African-American and Latino homeowners underwater to African-American and Latino home-ownership

NYC Total 32% 56% 1.75 53% 1.65Bronx 39% 60% 1.55 55% 1.40Brooklyn 57% 70% 1.23 70% 1.22Manhattan 11% 28% 2.66 19% 1.77Queens 32% 61% 1.87 54% 1.67Staten Island

14% 27% 1.88 29% 2.02

Suffolk 13% 30% 2.34 29% 2.21Nassau 16% Not available Xxx 37% 2.31Westchester 16% Not available Xxx 33% 2.14

4 Nassau county pre-foreclosure notice figures were not available from ListSource. 5 The need for mortgage modifications will continue since New York homeprices prices were down 1.61% according to the Case-Shiller index in October 2010, the most recent period for which this data exists.

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For years, banks pushed predatory loans in African-American and Latino communities, which lead directly to today’s pre-foreclosures. The disproportionate share of current pre-foreclosure notices received by African-Americans and Latinos was not created in a vacuum. For years in the early and middle part of the last decade, banks and other lenders pushed predatory loans on members of the African-American and Latino communities. Data taken from the Home Mortgage Disclosure Act has shown that African-Americans and Latinos in Suffolk and Nassau counties have disproportionately gotten high-cost loans for years6. As far back as 2004, in both New York City and Long Island, lenders were four to five times more likely to issue high-cost loans to African-Americans and Latinos than they were to whites – and this fact held true for both purchase loans and refinance loans. A study of 2004 data7 shows that for the New York Metropolitan area, in comparative terms, African-American homebuyers were 5.5 times more likely to receive a high-cost purchase loan than whites. Latino homebuyers were 4.1 times more likely to receive a high-cost purchase loan than whites. For refinance loans, African-Americans were 5.6 times more likely to receive a high-cost refinance loan than whites. Latino homeowners were 4.2 times more likely to receive a high-cost refinance loan than whites. The story was the same on Long Island. For purchase loans made in 2004, African-American homebuyers were 5.1 times more likely to receive a high-cost loan than whites. Latino homebuyers were 3.9 times more likely to receive a high-cost loan than whites. For that same year (2004), lenders refinanced African-Americans’ mortgages with a high-cost loan 5.1 times more often than they did with white homeowners. Latino homeowners were 4.9 times more likely to receive a high-cost loan than whites. The relationship between years of predatory lending and the current foreclosure crisis can clearly be seen by looking, as an example, at the 2010 pre-foreclosure data and the 2006 high-cost loan data8 in Queens.

Of the 20 Queens census tracts that had the highest number of high-cost loans in 2006, 17 of them (85%) are in the Queens zip codes that had the highest number of pre-foreclosure notices in 2010.

6 HMDA data includes information if a loan was high cost, that is, originated at an APR three points above comparable Treasury Rates on first liens or five points above the Treasury Rate for second liens. Loans above these thresholds were reported with the “rate spread” or difference between the APR on the loan and the comparable treasury rate. 7 The High Cost of Credit, Disparities in High-priced Refinance Loans to Minority Homeowners in 125 American Cities, September 27, 2005, produced by ACORN – Association of Community Organizations for Reform Now. 8 The high cost loan data is from the report: “Foreclosure Exposure 2: The Cost to our Cities and Neighborhoods: Report for New York City”, October 24, 2007, published by ACORN – Association of Community Organizations for Reform Now

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Queens Census Tract

Corresponding Zip Code

Neighborhood Number of high cost

loans made in 2006

Is this zip code in 2010 one of the top 10 for African-American and Latino pre-foreclosure notices?

0334.02 11434 Springfield Gardens 196 YES 0358.00 11413 Springfield Gardens 175 YES 0656.00 11422 Rosedale 162 YES 0526.00 11412 Saint Albans 141 YES 0168.00 11420 South Ozone Park 134 YES 0258,00 11433 Jamaica 127 YES 0182.00 11420 South Ozone Park 126 YES 0292.00 11434 Springfield Gardens 119 YES 0664.00 11422 Rosedale 116 YES 1008.00 11691 Far Rockaway 115 YES 0578.00 11428 Queens Village 112 No0158.00 11419 South Richmond Hill 103 YES 0376.00 11412 Saint Albans 103 YES 0330.00 11434 Springfield Gardens 102 YES 0106.00 11419 South Richmond Hill 97 YES 0288.00 11434 Locust Manor 86 YES 0998.00 11691 Far Rockaway 84 YES 0363.00 11273 East Elmhurst 82 No0010.00 11421 Woodhaven 81 No0680.00 11413 Rosedale 77 YES Even in areas with similar incomes, African-American and Latinos are more likely to have received a pre-foreclosure notice. When examining comparable neighborhoods in Queens, Brooklyn, and Suffolk County, the differences between white neighborhoods and African-American and Latino neighborhoods are stark. Even in neighborhoods with similar incomes, African-Americans and Latinos are more likely to have received pre-foreclosure notices and are more likely to be underwater on their mortgages. Below are comparisons for the following pairs of neighborhoods (all figures are based on data in the listed zip codes): Queens Cambria Heights (zip 11411) and Fresh Meadows (11366) Queens Hollis (11429) and Forest Hills (11375) Brooklyn Canarsie (11236) and Bay Ridge (11209) Suffolk Wyandanch (11798) and Ridge (11961) Suffolk Central Islip (11722) and Deer Park (11961) For example, in Queens, both Cambria Heights and Fresh Meadows have comparable median household incomes of $79,561 and $82,940 respectively. Between 2000 and 2007 about 54% of the homeowners in Cambria Heights got new mortgages compared to 42% in Fresh Meadows – a

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difference of only 25%. In both areas, most residents are homeowners – 85% in Cambria Heights and 72% in Fresh Meadows. But this is where the similarities end. More than 90% (92.9%) of the homeowners in Cambria Heights’s zip code are African-American and Latino – in fact 89% of the residents are African-American, many immigrants from the Caribbean. Cambria Heights is one of the most affluent African-American metropolitan neighborhoods in the U.S. In comparison only 12.3% of homeowners in Fresh Meadow are African-American and Latino. Proportionally, homeowners in Cambria Heights are almost three and a half (3.5) times more likely to have gotten pre-foreclosure notices and over two and a half times (2.5) more likely to be underwater on their mortgages. Banks have taken possession of twice as many homes in Cambria Heights as in Fresh Meadows during 2010. Given the similarities of income and years during which home purchases were made, the differences clearly show the targeted nature of predatory lending and the need for modifications. Area Number

of Home-owners

Number of African-American and Latino Home-owners

Percentage of African-America and Latino Home-owners

Percentage of Home-owners who have received a pre-foreclosure notice

Ratio Percentage of home-owners underwater on their mortgages

Ratio Number of bank owned proper-ties in 2010

Cambria Heights

4,728 4,394 95.9% 1.9% 340% 21.4% 264% 6

Fresh Meadows

3,217 395 12.3% 0.6% 8.1% 3

Also in Queens, the Hollis and Forest Hills neighborhoods have comparable median household incomes of $68,666 and $67,862 respectively. But almost 90% (88.9%) of the homeowners in Hollis’ zip code are African-American and Latino which is one of the oldest neighborhoods for African-American homeowners with some families owning in the area since the 1950’s. Sixty-three percent of the area is made up of homeowners. In Forest Hills, in comparison, only 7.2% of homeowners are African-American or Latino. Proportionally, homeowners in Hollis are seven (7) times more likely to have gotten pre-foreclosure notices and almost four and a half (4.5) times more likely to be underwater on their mortgages. Banks have taken possession of 13 homes in Hollis and only 1 in Forrest Hills during 2010. Since twice as many of the mortgages in Hollis (44% compared to 21%) were issued in the peak of the predatory lending crisis, 2004 to 2007, it makes sense that, having been targeted for these high-cost loans, Hollis is now suffering the consequences. Area Number

of Home-owners






Hollis 4,872 4,330 88.9% 2.9% 732% 23.3% 440% 13Forest Hills 4,135 512 7.2% 0.4% 5.3% 1

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In Brooklyn, both Canarsie and Bay Ridge have comparable median household incomes of $54,595 and $59,933 respectively. They also have, compared to Brooklyn as a whole, a high percentage of homeowners – 51% in Canarsie and 75% in Bay Ridge. (Brooklyn is one of the most renter-dominated counties in the country.) The similarity in income, however, does not translate into a further parallel in pre-foreclosure notices, and once again race is a major difference in the two neighborhoods. About 84% of the homeowners in Canarsie’s zip code are African-American and Latino, compared with 6.1% in Bay Ridge’s zip code. Proportionally, homeowners in Canarsie are six and a half (6.5) times more likely to have gotten pre-foreclosure notices and almost four (4) times more likely to be underwater on their mortgages. Twice as many mortgages in Canarsie (47% compared with 23% in Bay Ridge) were purchased in the peak of the predatory lending crisis, 2004 to 2007. As in other examples throughout the downstate area, there is a clear correlation among race, predatory loans, and pre-foreclosure notices. Area Number

of Home-owners






Canarsie 13,435 11,280 84.0% 2.4% 666% 17.9% 395% 15Bay Ridge 7,475 456 6.1% 0.4% 4.5% 7 In Suffolk County, both Wyandanch and Ridge have comparable median household incomes of $51,500 and $56,000 respectively. In Wyandanch 71% of all households are owner-occupied compared to 92% in Ridge. Differences in the pre-foreclosure notices – which once again relate to racial demographics – persist in the two communities. More than 80% (83.9%) of the homeowners in Wyandanch are African-American and Latino (with some families having owned there since the mid 1950’s) , compared with 6.4% in Ridge. Proportionally, homeowners in Wyandanch are almost three and a half (3.5) times more likely to have gotten pre-foreclosure notices and almost twice as likely to be underwater on their mortgages. Banks have taken possession of 58 homes in Wyandanch during 2010 compared with only 15 in Ridge, even though Ridge has more homeowners. Since almost 20% of Wyandanch houses were purchased in the peak of the predatory lending crisis, 2004 to 2007, it makes sense that having been targeted for these high-cost loans, Wyandanch is now suffering the consequences. Area Number

of Home-owners






Wyandanch 2,859 2,398 83.9% 5.0% 357% 5.2% 179% 58Ridge 3,605 225 6.4% 1.4% 2.9% 15

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Deer Park and Central Islip is another pair of Suffolk County communities with similar size, percentage of homeowners compared to renters and income levels, but that also have a divergence in racial demographics and foreclosure rates. Central Islip has a median household income of $70,117 compared to Deer Park’s at $78,056. In both hamlets, about 46% of the homeowners with mortgages received their loans between 2000 and 2007 – the height of the housing bubble. In Deer Park 83% of the households are owner-occupied compared to 76% in Central Islip. However, when analyzing the racial demographic and pre-foreclosure data, it is clear there are more differences than similarities. Just over 50% of the homeowners in Central Islip are African-American or Latino, compared with 20.6% in Deer Park . Proportionally, homeowners in Central Islip are almost two and a half (2.5) times more likely to have gotten pre-foreclosure notices than in Deer Park, and they are twice as likely to be underwater on their mortgages. And despite having fewer homeowners than Deer Park, Central Islip had almost three times as many houses repossessed by the lenders in 2010 (65 for Central Islip to 25 for Deer Park). Because the income and house purchase timelines of the two hamlets are so similar, it is clear that predatory lenders were targeting African-Americans and Latinos for high-cost loans, and that there is a corresponding need for mortgage modifications in those communities. Area Number

of Home-owners






Central Islip 6,767 3,414 50.5% 4.9% 245% 5.3% 212% 65Deer Park 7,097 1462 20.6% 2.0% 2.5% 25 Specific African-American and Latino neighborhoods have the highest numbers of pre-foreclosure notices. Areas with a large population of African-American and Latino homeowners often have the highest number of pre-foreclosure notices, showing the disproportionate impact that the foreclosure crisis is having on these formerly stable neighborhoods. (The colored areas in the maps below correspond to the 10 zip codes identified in the charts above them.)

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In the Bronx, eight of the 10 zip codes with the largest number of pre-foreclosure notices are at least 55% African-American and Latino, and six of the 10 are more than two-thirds

African-American and Latino.

County Zip Code

Total Owner Occupied House-holds

Total African American and Latino Homeowners Owner Occupied House-holds

Percent African American and Latino Owner Occupied House-holds

Total Pre- Fore-closures Filed

Total African American and Latino House-holds in Pre fore-closure

Percent all houses in pre-fore-closure that are African American and Latino

Bronx 10469 9,188 6,130 66.7% 257 196 76.3%Bronx 10466 7,204 6,225 86.4% 230 202 87.8%Bronx 10473 4,909 3,927 80.0% 139 113 81.3%Bronx 10462 7,374 4,459 60.5% 138 85 61.6%Bronx 10465 6,882 1,896 27.6% 132 72 54.5%Bronx 10472 3,193 1,835 57.5% 114 62 54.4%Bronx 10467 3,369 2,276 67.6% 110 86 78.2%Bronx 10461 5,362 1,202 22.4% 91 32 35.2%Bronx 10460 1,680 1,163 69.2% 80 55 68.8%Bronx 10456 1,589 1,362 85.7% 78 70 89.7%

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In Brooklyn, of the 10 zip codes with the highest number of pre-foreclosure notices, eight are neighborhoods in which more than 70% of the homeowners are African-American or Latino.

County Zip Code







Kings 11208 8,045 5,633 70.0% 330 226 68.5%Kings 11236 13,435 11,280 84.0% 323 285 88.2%Kings 11207 7,831 6,828 87.2% 288 236 81.9%Kings 11221 6,018 5,224 86.8% 279 239 85.7%Kings 11234 17,676 6,274 35.5% 236 124 52.5%Kings 11233 5,394 4,798 89.0% 219 187 85.4%Kings 11212 4,375 3,991 91.2% 149 133 89.3%Kings 11216 4,026 3,442 85.5% 115 98 85.2%Kings 11210 7,277 3,632 49.9% 114 80 70.2%Kings 11213 3,718 2,756 74.1% 102 82 80.4%

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Of the 10 zip codes in Queens with the highest number of pre-foreclosure notices, in eight of the zip codes more than 50% of homeowners are African-American and Latino, and in six of the 10 areas are more than 80% African-American and Latino.

County Zip Code







Queens 11434 8,302 7,455 89.8% 284 241 84.9%Queens 11413 8,221 7,567 92.0% 221 192 86.9%Queens 11420 7,930 3,815 48.1% 210 105 50.0%Queens 11412 7,266 6,646 91.5% 205 183 89.3%Queens 11419 6,339 1,473 23.2% 160 51 31.9%Queens 11433 4,482 3,849 85.9% 157 133 84.7%Queens 11368 6,143 3,352 54.6% 156 117 75.0%Queens 11422 5,684 4,879 85.8% 153 134 87.6%Queens 11691 4,482 2,805 62.6% 152 123 80.9%Queens 11429 4,872 4,330 88.9% 140 124 88.6%

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African-Americans and Latinos are often unable to get mortgage modifications or mortgage refinance loans. Although the data clearly show that African-Americans and Latinos have had endured the worst effects of the foreclosure crisis in the New York metropolitan area, they are not able to get mortgage modifications or to get refinance loans at the same rates as whites. Due to a variety of factors, including the drop in property values, African-Americans and Latinos are getting very few mortgage refinancing loans in New York City. A recently released paper by the Furman Center examined the new mortgages and mortgage refinance loans reported by all lenders through the Home Mortgage Act Data (HMDA) for 2009. Their examination shows that while Whites and Asians had large increases in mortgage refinance loans, African-Americans and Latinos did not. In particular the report states:

“…while approximately 19 percent of all New York homeowners are black, these hom-eowners obtained only about 13 percent of the mortgage refinancings originated in 2009. … “The disparities in refinancing trends in 2009 would likely have been even greater without the availability of FHA/VA-backed refinancing loans. …39 percent of all refinancing loans issued to black homeowners in New York City in 2009 were FHA/VA-backed, compared to only five percent for white homeowners and three percent for Asian homeowners.”9

When families then ask for loan modifications, there are lots of problems as they seek to get them. The papers are full of these stories, and informal discussions with loan counseling operations in New York City show that, at most, 16% of households who apply for permanent modifications get them, often leaving those families worse off due to accumulated late fees and interest charges. Families who have tried to modify their mortgages report disconnected calls, lost paperwork, and misleading information given to them by the servicers, among other problems. The following two stories involve homeowners trying to get modifications and illustrate the resistance that families face from banks.

In November 2006, Ms. Antoine and her husband bought their first house, a one-family, and moved with their teenage son from their apartment in Flatbush to Hollis, Queens. The problems with the bank started before the closing documents were even signed: Ms. Antoine received an option ARM (Adjustable Rate Mortgage), which she had not been properly informed about and in fact did not know about until after closing on her home. Not only was the interest rate set at 7 percent, but it was scheduled to increase in 2010. For the first couple years, they had no problems making their payments of $2,196 a month. But after the financial crash of 2008, Ms. Antoine’s hours as a social worker in the city were reduced, and her husband, who works as a taxi driver, had a schedule that became more irregular, as more and more people cut costs by taking public

9 www.furmancenter.org, Mortgage Lending During the Great Recession 2009, page 8.

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transportation. Their son, who had started attending John Jay College, got a part-time job, but that was hardly enough to make up the difference. As a result, the family fell behind on their mortgage. After receiving a foreclosure notice, the family submitted a proposal for a hardship modification in August 2009, which would lower their monthly payments to an affordable $1,654 a month. More than a year ago, on December 16, 2009, Chase gave them a HAMP trial modification. It was supposed to last for three months. But Chase, as it has done to so many other homeowners, stopped communicating at the end of the trial period. Since the family had not heard about a permanent modification at the end of three-month trial, they tried to contact the bank; they got no response. Chase has since requested updated documents every month. The family has also had to go to court every month starting in March 2010 to show that they have been sending the paperwork to Chase. Chase consistently claims not to have received any of the documents submitted, and so the family keeps resubmitting documents. No permanent modification has been offered, despite the fact that the family has met all its obligations under the trial modification. The family keeps receiving modification applications at their home even though this paperwork has already been submitted. In October 2010, Ms. Antoine called Chase, and a Chase representative told that her that the family’s documents were outdated (which they were not), and so the family resubmitted the documents yet again. Ms. Antoine is still making mortgage payments based on her trial modification and is hoping for a permanent modification so that she will have security in her home and can stop going to court every month. It has been almost a year and a half since the initial application. Mr. and Mrs. Hickson were first-time homebuyers in 1999 when they moved to Coram in Suffolk County from their apartment in Jamaica. Their loan with Countrywide had a relatively high interest rate – 7.5 percent – but the principal was only $195,000. However, when the Hicksons fell behind after their son was born 25 weeks premature and Mrs. Hickson couldn’t travel to her job as a social worker for New York City, the bank refused to take their partial payments, and instead pushed them into refinancing. The representative from Countrywide promised them that it was not a big deal, that it would fix their problems, and that it was the only option. This process repeated itself a couple years later, when a mortgage representative from Countrywide came to their house in the middle of a reception for Mr. Hickson’s sister’s wedding. After twice being convinced to refinance, the principal on their loan with Countrywide had risen to $376,135. In 2007, their oldest son got into a serious car accident a couple weeks after his 17th birthday; he is now in a vegetative state. Soon after, Mr. Hickson, a truck driver, got laid off from his position at Florence Building Supplies. At that point, the family had fallen behind on the mortgage. They often attempted to give Countrywide, and later Bank of America, payments to pay down their arrears, but, every time, the lender would refuse. Being desperate, they spent thousands of dollars at for-profit modification companies that did nothing to help them. In May 2009, they submitted a loan modification request through MHANY Management. For seven months, the bank did nothing. In December, the Hicksons were told that the proposal was with a negotiator and they should have an answer shortly. One month later, the bank requested updated documentation. When Mrs. Hickson submitted what had been asked for – one week after the request was placed – they learned that their house was up for sale in less than a month. Since then, MHANY

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has been able to postpone the sale of the house four times. Three times, the bank asked for new or updated documents. The Hicksons are living in a constant state of limbo, and don’t know from month to month whether they will be able to stay in their home.

As these stories illustrate, the modification process is not working at Chase and Bank of America, which are the fourth- and first-largest mortgage servicers, respectively. There are plenty of stories like these in New York City and Long Island for families who are trying to work with other servicers as well. Families are not able to understand or work with the process, whether they started the process a year ago or three months ago. Recommendations The homeownership gains of African-Americans and Latinos need to be protected in the city of New York as well as in Nassau, Suffolk and Westchester counties. The stability of these neighborhoods must be preserved. Banks and servicers must modify mortgages immediately. The modifications must be based on principal reduction. The third- and fourth-largest servicers of mortgages, JP Morgan Chase and CitiBank, are located right here in New York City and need to especially step forward to deal with this challenge. Banks and servicers must: 1) Provide principal reduction for homeowners who are underwater for as much of the principal as possible without triggering a negative net present value. 2) Offer sustainable long-term loan modifications to homeowners with a permanent affordable interest rate. The modification must be sustainable and include lowering the interest rate, taking into account household debts and medical expenses, with the lowered interest rate in place for the life of loan. 3) Evaluate the modification requests based on the arrearages as of the first date the modification was asked for. 4) Waive all late fees when a loan modification is provided, so that all available funds can be applied to the principal and interest of the loan. 5) Promptly respond to, evaluate, and decide on modification requests within 30 days. 6) Provide a specific explanation of any rejection of a modification request so that the family may be able to meet the requirements in some way or at least discuss the problems. This explanation must include the actual calculations used by the servicer or bank including a comparison between the amounts of money that would be received by investors if a foreclosure were to occur as compared to the amount investors would receive under the proposed modification. Also included must be the specifics of which institution and which person in the institution decided that a modification proposal was to be denied. When a servicer believes a PSA prevents an NPV-negative modification, the servicer shall contact the trustee and any other parties authorized under the terms of the PSA to grant a waiver, whether individual investors,

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credit rating agencies, bond insurers, or otherwise, in order to obtain permission to perform a HAMP modification. The servicer shall provide the borrower or the borrower’s representative a copy of the limiting language in the PSA, a copy of all correspondence with the lender and investors attempting to obtain authority to perform a modification, and electronic access to a complete and unaltered copy of the PSA. 7) Have a single person as the point person for the modification discussion and follow up. 8) Not start foreclosure procedures while a modification request is pending and, in cases where foreclosure proceedings have been initiated, halt foreclosure activity while a modification request is pending. 9) Allow homeowners to apply for modifications before they become delinquent. 10) Send a mortgage modification package and a list of HUD Approved Housing agencies providing foreclosure counseling to homeowners at the point that the mortgage is reported to the New York State Banking Commission under the 90-day pre-foreclosure notice law.

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Appendix The cost of foreclosures on African-American and Latino communities. Once people fall behind on their mortgages, families then start to default and lose their homes to foreclosures. Foreclosures lead to a number of problems:

1. additional foreclosures 2. crime 3. the unnecessary expenditure of city funds to fight neighborhood blight, and 4. depressed home prices.

Foreclosures in a community lead to additional foreclosures and defaults in that area. A 2010 report by the Furman Center, which examined data from New York City, shows that “default rates increase with the rate of foreclosure notices and the number of lender-owned properties (REOs) in the tract.”10 Once a homeowner is facing foreclosure, a fair number of them abandon their homes leaving them vacant. Often when the bank does foreclose on a property they evict the occupants and leave it vacant. Therefore the increase in foreclosures leads to increased crime. In a publication of the US Department of Justice, data analyzed by the Charlotte-Mecklenburg Police Department showed that:

“Foreclosures lead to higher crime rates. The CMPD analyzed rates of violent crime, property crime, and 911 service calls in all neighborhoods in the study from 2003 to 2006. It found the following: !

• "#$%&'(!)*#+&!*$,&!)$',#,(&'(%-!./*#'0!(1&!23-&4*!5&*#$.!#'!(1&!1#0136$*&)%$,/*&!'&#017$*1$$.,8!7/(!*&+4#'&.!,#0'#6#)4'(%-!%$9&*!#'!(1&!%$9!6$*&)%$,/*&!'&#017$*1$$.,8!&:)&5(!#'!;<<=>!

• ?*$5&*(-!)*#+&!4'.!*&%4(&.!,&*@#)&!)4%%,!,5#A&.!#'!;<<=8!6&%%!,14*5%-!#'!;<<28!4'.!*$,&!,%#01(%-!#'!;<<B8!6$%%$9#'0!4!54((&*'!,#+#%4*!($!@#$%&'(!)*#+&!*4(&,!#'!%$936$*&)%$,/*&!'&#017$*1$$.,>!C1#,!,&&+,!($!)$**&,5$'.!($!4!D/#)A!#')*&4,&!#'!1$/,#'0!/'#(,!(14(!-&4*>!E!'/+7&*!$6!(1&,&!'&#017$*1$$.,!9&*&!7/#%(!#'!;<<=8!4'.!+4'-!1$+&,!&:5&*#&')&.!455%#4')&!4'.!7/#%.#'0!+4(&*#4%!(1&6(,>!!Some of the differences in the crime statistics!observed may be due to when the houses were built. The high-foreclosure neighborhoods were built predominantly between 1999 and 2003 and the low-foreclosure neighborhoods from 2003 to 2006; therefore, the low-foreclosure group might simply be at an earlier stage of the foreclosure process, and may have not yet experienced the subsequent impact on crime and disorder.”11

Another study, by Professor Dan Immergluck of Georgia Tech and Woodstock researcher Geoff Smith, shows that an increase in the foreclosure rate to about 2.8 foreclosures for every 100 owner-occupied properties in one year corresponds to an increase in neighborhood violent crime

10“The Role of Neighborhood Characteristics in Mortgage Default Risk: Evidence from New York City”, Page 3 , found at http://furmancenter.org/files/publications/working_paper.pdf 11 http://www.ojp.usdoj.gov/BJA/topics/GPSNewsletter.pdf, page 3

16

of approximately 6.7%12. Communities can ill afford yet another factor that precipitates higher crime rates. Increased foreclosures and vacant houses also lead to higher costs for cities. Except where the house in question never becomes vacant, it has been estimated that each foreclosure costs a city between $400 and $34,00013 – since the city has to use scare resources to take care of the problems caused by having a vacant structure. These costs include the police department to deal with crime, the code enforcement department to deal with an unsecured building and unkempt property, the city’s legal department and others. In the worst case, the building might catch on fire endangering the rest of the neighborhood. Properties that have been foreclosed upon have been shown to decrease the value of other homes in the neighborhood. A study of Chicago data that looks at foreclosure and single-family home sales in 1997 and 199814 calculated that the foreclosure of one single-family home depresses the property values of each home within one-eighth of a mile (or one city block) by an average of 0.9%; a single foreclosure causes home values to decrease even more in low- to moderate-income communities (1.4%). If the average house value is $400,000, then each foreclosure in the area costs each of the other homeowners $3,600. If a neighborhood has 10 foreclosures, which is all too common, then each remaining homeowners has lost $36,000 in value. !

12 Dan Immergluck and Geoff Smith, “The Impact of Single-Family Mortgage Foreclosures on Neighborhood Crime,” Housing Studies, Vol. 212, No. 6, November 2006. 13 “The Municipal Cost of Foreclosures: A Chicago Case Study “,Apgar, William, Duda, Mark 14 Dan Immergluck and Geoff Smith, “The External Costs of Foreclosure: The Impact of Single-Family Mortgage Foreclosures on Property Values,” Housing Policy Debate, Vol. 17, Issue 1.

Charts

County Zip!Code

Total!Owner!Occupied!House"holds

Total!African!American!and!Latino!Homeowners!Owner!Occupied!House"holds

Percent!African!American!and!Latino!Owner!Occupied!House"holds

Total!Pre"!Fore"closureNotices!Filed!

Percent"age!of!homes!in!pre!"fore"closure!

Total!African!American!and!Latino!House"holds!in!Pre!fore"closure

Percent!all!houses!in!pre"fore"closure!that!are!African!American!and!Latino

Ratio!of!%!African"American!and!Latino!pre"fore"closure!notices!to!African"American!home"ownership

Total!House"holds!Under!Water

Percent!House"holds!Under!Water

Total!African!American!and!Latino!House"holds!Under!Water

Percent!All!under!water!House"holds!that!are!African!American!or!Latino

Ratio!of!%!African"American!and!Latino!home"owners!underwater!to!African"American!home"ownership

8!County!Region TOTALS 1,285,239 318,266 24.8% 145,181 11.3% 67,601 46.6% 1.96!County!Region! TOTALS 1,127,131 282,397 25.1% 18,212 1.6% 8,452 46.4% 1.9

New!York!City TOTALS 757,376 233,846 30.9% 11,111 1.5% 6,274 56.5% 1.8 92,199 12.2% 48,662 52.8% 1.7Bronx TOTALS 69,675 39,760 57.1% 1,771 2.5% 1,243 70.2% 1.2 12,213 17.5% 8,532 69.9% 1.2Bronx 10451 807 637 78.9% 8 1.0% 7 87.5% 1.1 179 22.2% 154 86.0% 1.1Bronx 10452 576 480 83.3% 21 3.6% 18 85.7% 1.0 103 17.9% 83 80.6% 1.0Bronx 10453 1,014 882 87.0% 32 3.2% 30 93.8% 1.1 212 20.9% 192 90.6% 1.0Bronx 10454 702 483 68.8% 23 3.3% 15 65.2% 0.9 131 18.7% 87 66.4% 1.0Bronx 10455 845 636 75.3% 33 3.9% 24 72.7% 1.0 159 18.8% 105 66.0% 0.9Bronx 10456 1,589 1,362 85.7% 78 4.9% 70 89.7% 1.0 367 23.1% 317 86.4% 1.0Bronx 10457 1,443 1,098 76.1% 56 3.9% 38 67.9% 0.9 353 24.5% 282 79.9% 1.0Bronx 10458 1,271 696 54.8% 43 3.4% 21 48.8% 0.9 293 23.1% 179 61.1% 1.1Bronx 10459 1,704 1,387 81.4% 55 3.2% 46 83.6% 1.0 332 19.5% 268 80.7% 1.0Bronx 10460 1,680 1,163 69.2% 80 4.8% 55 68.8% 1.0 405 24.1% 283 69.9% 1.0Bronx 10461 5,362 1,202 22.4% 91 1.7% 32 35.2% 1.6 728 13.6% 306 42.0% 1.9Bronx 10462 7,374 4,459 60.5% 138 1.9% 85 61.6% 1.0 1,026 13.9% 655 63.8% 1.1Bronx 10463 3,637 952 26.2% 29 0.8% 14 48.3% 1.8 205 5.6% 81 39.5% 1.5Bronx 10464 1,068 111 10.4% 10 0.9% 2 20.0% 1.9 76 7.1% 10 13.2% 1.3Bronx 10465 6,882 1,896 27.6% 132 1.9% 72 54.5% 2.0 991 14.4% 436 44.0% 1.6Bronx 10466 7,204 6,225 86.4% 230 3.2% 202 87.8% 1.0 1,761 24.4% 1556 88.4% 1.0

County Zip!Code














Bronx 10467 3,369 2,276 67.6% 110 3.3% 86 78.2% 1.2 824 24.5% 631 76.6% 1.1Bronx 10468 962 491 51.0% 17 1.8% 9 52.9% 1.0 135 14.0% 77 57.0% 1.1Bronx 10469 9,188 6,130 66.7% 257 2.8% 196 76.3% 1.1 1,761 19.2% 1350 76.7% 1.1Bronx 10470 1,733 661 38.1% 33 1.9% 21 63.6% 1.7 262 15.1% 147 56.1% 1.5Bronx 10471 2,418 300 12.4% 12 0.5% 5 41.7% 3.4 88 3.6% 10 11.4% 0.9Bronx 10472 3,193 1,835 57.5% 114 3.6% 62 54.4% 0.9 659 20.6% 383 58.1% 1.0Bronx 10473 4,909 3,927 80.0% 139 2.8% 113 81.3% 1.0 993 20.2% 813 81.9% 1.0Bronx 10474 345 235 68.1% 16 4.6% 10 62.5% 0.9 100 29.0% 76 76.0% 1.1Bronx 10475 400 236 59.0% 14 3.5% 10 71.4% 1.2 70 17.5% 51 72.9% 1.2

County Zip!Code














Kings TOTALS 231,448 78,671 34.0% 3,403 1.5% 2054 60.4% 1.8 25,984 11.2% 14174 54.5% 1.6Kings 11201 6,371 601 9.4% 8 0.1% 2 25.0% 2.7 522 8.2% 56 10.7% 1.1Kings 11204 17,625 287 1.6% 60 0.3% 2 3.3% 2.0 482 2.7% 24 5.0% 3.1Kings 11205 3,125 1,377 44.1% 48 1.5% 24 50.0% 1.1 439 14.0% 204 46.5% 1.1Kings 11206 3,381 1,381 40.8% 76 2.2% 36 47.4% 1.2 614 18.2% 251 40.9% 1.0Kings 11207 7,831 6,828 87.2% 288 3.7% 236 81.9% 0.9 2,063 26.3% 1791 86.8% 1.0Kings 11208 8,045 5,633 70.0% 330 4.1% 226 68.5% 1.0 2,127 26.4% 1594 74.9% 1.1Kings 11209 7,475 456 6.1% 27 0.4% 1 3.7% 0.6 338 4.5% 32 9.5% 1.6Kings 11210 7,277 3,632 49.9% 114 1.6% 80 70.2% 1.4 791 10.9% 456 57.6% 1.2Kings 11211 7,126 920 12.9% 55 0.8% 7 12.7% 1.0 755 10.6% 85 11.3% 0.9Kings 11212 4,375 3,991 91.2% 149 3.4% 133 89.3% 1.0 910 20.8% 828 91.0% 1.0Kings 11213 3,718 2,756 74.1% 102 2.7% 82 80.4% 1.1 610 16.4% 460 75.4% 1.0Kings 11214 8,581 371 4.3% 31 0.4% 4 12.9% 3.0 503 5.9% 47 9.3% 2.2Kings 11215 8,303 1,252 15.1% 30 0.4% 10 33.3% 2.2 567 6.8% 68 12.0% 0.8Kings 11216 4,026 3,442 85.5% 115 2.9% 98 85.2% 1.0 737 18.3% 650 88.2% 1.0Kings 11217 3,552 1,042 29.3% 20 0.6% 12 60.0% 2.0 216 6.1% 58 26.9% 0.9Kings 11218 6,451 990 15.3% 86 1.3% 11 12.8% 0.8 487 7.5% 80 16.4% 1.1Kings 11219 6,864 228 3.3% 92 1.3% 0 0.0% 439 6.4% 18 4.1% 1.2Kings 11220 6,636 1,642 24.7% 27 0.4% 15 55.6% 2.2 393 5.9% 135 34.4% 1.4Kings 11221 6,018 5,224 86.8% 279 4.6% 239 85.7% 1.0 1,546 25.7% 1335 86.4% 1.0Kings 11222 3,530 296 8.4% 38 1.1% 0 0.0% 305 8.6% 31 10.2% 1.2Kings 11223 8,226 350 4.3% 67 0.8% 8 11.9% 2.8 456 5.5% 30 6.6% 1.5Kings 11224 3,052 578 18.9% 30 1.0% 11 36.7% 1.9 214 7.0% 57 26.6% 1.4

County Zip!Code














Kings 11225 3,040 2,317 76.2% 61 2.0% 53 86.9% 1.1 285 9.4% 199 69.8% 0.9Kings 11226 3,837 3,058 79.7% 86 2.2% 68 79.1% 1.0 544 14.2% 462 84.9% 1.1Kings 11228 6,842 394 5.8% 24 0.4% 1 4.2% 0.7 401 5.9% 36 9.0% 1.6Kings 11229 10,881 493 4.5% 96 0.9% 4 4.2% 0.9 847 7.8% 48 5.7% 1.3Kings 11230 6,923 365 5.3% 67 1.0% 3 4.5% 0.8 483 7.0% 33 6.8% 1.3Kings 11231 3,644 474 13.0% 19 0.5% 5 26.3% 2.0 248 6.8% 29 11.7% 0.9Kings 11232 1,863 816 43.8% 16 0.9% 12 75.0% 1.7 157 8.4% 72 45.9% 1.0Kings 11233 5,394 4,798 89.0% 219 4.1% 187 85.4% 1.0 1,259 23.3% 1101 87.5% 1.0Kings 11234 17,676 6,274 35.5% 236 1.3% 124 52.5% 1.5 2,433 13.8% 1119 46.0% 1.3Kings 11235 9,586 821 8.6% 90 0.9% 9 10.0% 1.2 688 7.2% 89 12.9% 1.5Kings 11236 13,435 11,280 84.0% 323 2.4% 285 88.2% 1.1 2,399 17.9% 2182 91.0% 1.1Kings 11237 2,164 1,140 52.7% 32 1.5% 21 65.6% 1.2 282 13.0% 163 57.8% 1.1Kings 11238 4,181 2,869 68.6% 62 1.5% 45 72.6% 1.1 375 9.0% 291 77.6% 1.1Kings 11239 274 235 85.8% 0 0.0% 0 N/A 60 21.9% 56 93.3% 1.1Kings 11243 120 60 50.0% 0 0.0% 0 N/A 9 7.5% 4 44.4% 0.9

County Zip!Code














New!York TOTALS 64,925 6,936 10.7% 190 0.3% 54 28.4% 2.7 2,037 3.1% 385 18.9% 1.8New!York 10001 744 68 9.1% 0 0.0% 0 N/A 26 3.5% 2 7.7% 0.8New!York 10002 1,240 80 6.5% 3 0.2% 0 0.0% 25 2.0% 2 8.0% 1.2New!York 10003 2,672 124 4.6% 2 0.1% 0 0.0% 27 1.0% 0 0.0% 0.0New!York 10004 370 38 10.3% 5 1.4% 0 0.0% 26 7.0% 3 11.5% 1.1New!York 10005 711 39 5.5% 7 1.0% 2 28.6% 5.2 54 7.6% 3 5.6% 1.0New!York 10006 159 10 6.3% 2 1.3% 0 0.0% 21 13.2% 2 9.5% 1.5New!York 10007 385 11 2.9% 1 0.3% 0 0.0% 18 4.7% 0 0.0% 0.0New!York 10009 694 43 6.2% 2 0.3% 0 0.0% 25 3.6% 1 4.0% 0.6New!York 10010 1,447 53 3.7% 17 1.2% 0 0.0% 48 3.3% 0 0.0% 0.0New!York 10011 3,603 261 7.2% 5 0.1% 1 20.0% 2.8 79 2.2% 13 16.5% 2.3New!York 10012 961 22 2.3% 0 0.0% 0 N/A 19 2.0% 1 5.3% 2.3New!York 10013 1,836 50 2.7% 3 0.2% 0 0.0% 58 3.2% 2 3.4% 1.3New!York 10014 2,607 114 4.4% 4 0.2% 1 25.0% 5.7 67 2.6% 3 4.5% 1.0New!York 10016 3,892 217 5.6% 20 0.5% 0 0.0% 120 3.1% 5 4.2% 0.7New!York 10017 1,567 109 7.0% 0 0.0% 0 N/A 37 2.4% 4 10.8% 1.6New!York 10018 158 11 7.0% 0 0.0% 0 N/A 3 1.9% 0 0.0%New!York 10019 2,539 163 6.4% 7 0.3% 0 0.0% 82 3.2% 8 9.8% 1.5New!York 10021 3,057 118 3.9% 7 0.2% 0 0.0% 70 2.3% 1 1.4% 0.4New!York 10022 3,075 176 5.7% 4 0.1% 1 25.0% 4.4 42 1.4% 1 2.4% 0.4New!York 10023 5,009 222 4.4% 5 0.1% 1 20.0% 4.5 90 1.8% 8 8.9% 2.0New!York 10024 3,283 144 4.4% 5 0.2% 2 40.0% 9.1 72 2.2% 4 5.6% 1.3

County Zip!Code














New!York 10025 3,830 271 7.1% 11 0.3% 0 0.0% 118 3.1% 9 7.6% 1.1New!York 10026 1,396 870 62.3% 9 0.6% 7 77.8% 1.2 117 8.4% 79 67.5% 1.1New!York 10027 1,020 616 60.4% 12 1.2% 12 100.0% 1.7 77 7.5% 50 64.9% 1.1New!York 10028 2,367 110 4.6% 3 0.1% 0 0.0% 52 2.2% 1 1.9% 0.4New!York 10029 487 160 32.9% 5 1.0% 3 60.0% 1.8 17 3.5% 5 29.4% 0.9New!York 10030 568 394 69.4% 11 1.9% 9 81.8% 1.2 64 11.3% 55 85.9% 1.2New!York 10031 730 505 69.2% 5 0.7% 5 100.0% 1.4 64 8.8% 50 78.1% 1.1New!York 10032 483 313 64.8% 0 0.0% 0 N/A 16 3.3% 13 81.3% 1.3New!York 10033 598 105 17.6% 1 0.2% 0 0.0% 9 1.5% 4 44.4% 2.5New!York 10034 323 60 18.6% 0 0.0% 2 N/A 8 2.5% 2 25.0% 1.3New!York 10035 674 430 63.8% 4 0.6% 3 75.0% 1.2 43 6.4% 25 58.1% 0.9New!York 10036 1,035 60 5.8% 6 0.6% 0 0.0% 45 4.3% 1 2.2% 0.4New!York 10037 128 88 68.8% 4 3.1% 3 75.0% 1.1 13 10.2% 9 69.2% 1.0New!York 10038 573 31 5.4% 0 0.0% 0 N/A 30 5.2% 2 6.7% 1.2New!York 10039 491 309 62.9% 0 0.0% 0 N/A 0 0.0% 0 N/ANew!York 10040 455 74 16.3% 0 0.0% 0 N/A 7 1.5% 1 14.3% 0.9New!York 10044 225 26 11.6% 0 0.0% 0 N/A 18 8.0% 2 11.1% 1.0New!York 10065 2,121 124 5.8% 3 0.1% 1 33.3% 5.7 42 2.0% 1 2.4% 0.4New!York 10069 572 21 3.7% 4 0.7% 0 0.0% 24 4.2% 3 12.5% 3.4New!York 10075 2,146 73 3.4% 0 0.0% 0 N/A 63 2.9% 1 1.6% 0.5New!York 10128 3,841 180 4.7% 7 0.2% 0 0.0% 138 3.6% 5 3.6% 0.8New!York 10172 52 0 0.0% 0 0.0% 0 N/A 0 0.0% 0 N/ANew!York 10280 801 43 5.4% 6 0.7% 1 16.7% 3.1 63 7.9% 4 6.3% 1.2

County Zip!Code














Queens TOTALS 288,910 93,751 32.4% 4,070 1.4% 2469 60.7% 1.9 41,431 14.3% 22513 54.3% 1.7Queens 11001 6,419 825 12.9% 14 0.2% 6 42.9% 3.3 582 9.1% 147 25.3% 2.0Queens 11004 2,278 187 8.2% 14 0.6% 0 0.0% 227 10.0% 23 10.1% 1.2Queens 11005 293 10 3.4% 0 0.0% 0 N/A 0 0.0% 0 N/AQueens 11040 11,218 939 8.4% 17 0.2% 5 29.4% 3.5 719 6.4% 136 18.9% 2.3Queens 11101 2,463 347 14.1% 13 0.5% 2 15.4% 1.1 306 12.4% 51 16.7% 1.2Queens 11102 2,412 378 15.7% 16 0.7% 4 25.0% 1.6 193 8.0% 44 22.8% 1.5Queens 11103 2,652 274 10.3% 11 0.4% 3 27.3% 2.6 140 5.3% 27 19.3% 1.9Queens 11104 1,426 198 13.9% 9 0.6% 2 22.2% 1.6 96 6.7% 12 12.5% 0.9Queens 11105 4,074 397 9.7% 18 0.4% 3 16.7% 1.7 198 4.9% 31 15.7% 1.6Queens 11106 1,925 323 16.8% 14 0.7% 4 28.6% 1.7 128 6.6% 43 33.6% 2.0Queens 11109 220 8 3.6% 0 0.0% 0 N/A 0 0.0% 0 N/AQueens 11354 5,450 274 5.0% 34 0.6% 3 8.8% 1.8 380 7.0% 37 9.7% 1.9Queens 11355 7,556 391 5.2% 31 0.4% 7 22.6% 4.4 368 4.9% 31 8.4% 1.6Queens 11356 3,744 708 18.9% 59 1.6% 36 61.0% 3.2 523 14.0% 195 37.3% 2.0Queens 11357 8,176 681 8.3% 27 0.3% 4 14.8% 1.8 453 5.5% 68 15.0% 1.8Queens 11358 7,020 615 8.8% 50 0.7% 8 16.0% 1.8 492 7.0% 82 16.7% 1.9Queens 11360 3,618 233 6.4% 9 0.2% 1 11.1% 1.7 228 6.3% 25 11.0% 1.7Queens 11361 5,134 598 11.6% 32 0.6% 7 21.9% 1.9 406 7.9% 74 18.2% 1.6Queens 11362 3,459 200 5.8% 15 0.4% 1 6.7% 1.2 243 7.0% 18 7.4% 1.3Queens 11363 1,616 108 6.7% 7 0.4% 0 0.0% 89 5.5% 8 9.0% 1.3Queens 11364 6,022 373 6.2% 25 0.4% 2 8.0% 1.3 333 5.5% 28 8.4% 1.4Queens 11365 6,019 488 8.1% 41 0.7% 10 24.4% 3.0 553 9.2% 76 13.7% 1.7

County Zip!Code














Queens 11366 3,217 395 12.3% 18 0.6% 3 16.7% 1.4 261 8.1% 53 20.3% 1.7Queens 11367 4,879 368 7.5% 32 0.7% 2 6.3% 0.8 362 7.4% 48 13.3% 1.8Queens 11368 6,143 3,352 54.6% 156 2.5% 117 75.0% 1.4 1,105 18.0% 734 66.4% 1.2Queens 11369 4,309 3,321 77.1% 123 2.9% 105 85.4% 1.1 948 22.0% 803 84.7% 1.1Queens 11370 4,130 1,187 28.7% 38 0.9% 17 44.7% 1.6 446 10.8% 188 42.2% 1.5Queens 11372 2,742 992 36.2% 27 1.0% 17 63.0% 1.7 222 8.1% 109 49.1% 1.4Queens 11373 6,820 1,295 19.0% 49 0.7% 25 51.0% 2.7 422 6.2% 150 35.5% 1.9Queens 11374 4,122 363 8.8% 30 0.7% 6 20.0% 2.3 246 6.0% 41 16.7% 1.9Queens 11375 7,135 512 7.2% 28 0.4% 8 28.6% 4.0 377 5.3% 42 11.1% 1.6Queens 11377 6,095 1,340 22.0% 58 1.0% 22 37.9% 1.7 581 9.5% 202 34.8% 1.6Queens 11378 5,882 863 14.7% 51 0.9% 26 51.0% 3.5 717 12.2% 256 35.7% 2.4Queens 11379 7,469 667 8.9% 36 0.5% 11 30.6% 3.4 616 8.2% 134 21.8% 2.4Queens 11385 10,403 2,737 26.3% 106 1.0% 57 53.8% 2.0 1,215 11.7% 570 46.9% 1.8Queens 11411 4,728 4,394 92.9% 90 1.9% 82 91.1% 1.0 1,014 21.4% 933 92.0% 1.0Queens 11412 7,266 6,646 91.5% 205 2.8% 183 89.3% 1.0 1,885 25.9% 1706 90.5% 1.0Queens 11413 8,221 7,567 92.0% 221 2.7% 192 86.9% 0.9 1,990 24.2% 1845 92.7% 1.0Queens 11414 5,313 598 11.3% 45 0.8% 14 31.1% 2.8 564 10.6% 127 22.5% 2.0Queens 11415 1,242 177 14.3% 14 1.1% 0 0.0% 122 9.8% 24 19.7% 1.4Queens 11416 2,950 1,010 34.2% 83 2.8% 35 42.2% 1.2 547 18.5% 265 48.4% 1.4Queens 11417 4,901 1,336 27.3% 89 1.8% 36 40.4% 1.5 883 18.0% 385 43.6% 1.6Queens 11418 4,250 1,356 31.9% 113 2.7% 51 45.1% 1.4 1,626 38.3% 351 21.6% 0.7Queens 11419 6,339 1,473 23.2% 160 2.5% 51 31.9% 1.4 1,308 20.6% 349 26.7% 1.1Queens 11420 7,930 3,815 48.1% 210 2.6% 105 50.0% 1.0 1,827 23.0% 1008 55.2% 1.1Queens 11421 5,261 2,427 46.1% 123 2.3% 83 67.5% 1.5 1,257 23.9% 834 66.3% 1.4

County Zip!Code














Queens 11422 5,684 4,879 85.8% 153 2.7% 134 87.6% 1.0 1,314 23.1% 1180 89.8% 1.0Queens 11423 4,423 2,113 47.8% 85 1.9% 46 54.1% 1.1 756 17.1% 457 60.4% 1.3Queens 11426 3,911 574 14.7% 30 0.8% 8 26.7% 1.8 443 11.3% 113 25.5% 1.7Queens 11427 3,762 1,008 26.8% 36 1.0% 14 38.9% 1.5 427 11.4% 152 35.6% 1.3Queens 11428 3,733 1,719 46.0% 74 2.0% 43 58.1% 1.3 691 18.5% 337 48.8% 1.1Queens 11429 4,872 4,330 88.9% 140 2.9% 124 88.6% 1.0 1,133 23.3% 1003 88.5% 1.0Queens 11432 5,331 1,031 19.3% 69 1.3% 15 21.7% 1.1 681 12.8% 162 23.8% 1.2Queens 11433 4,482 3,849 85.9% 157 3.5% 133 84.7% 1.0 1,497 33.4% 1291 86.2% 1.0Queens 11434 8,302 7,455 89.8% 284 3.4% 241 84.9% 0.9 2,569 30.9% 2273 88.5% 1.0Queens 11435 4,590 2,337 50.9% 103 2.2% 64 62.1% 1.2 950 20.7% 613 64.5% 1.3Queens 11436 3,580 3,024 84.5% 120 3.4% 100 83.3% 1.0 1,238 34.6% 1011 81.7% 1.0Queens 11691 4,482 2,805 62.6% 152 3.4% 123 80.9% 1.3 1,272 28.4% 967 76.0% 1.2Queens 11692 1,566 1,155 73.8% 58 3.7% 46 79.3% 1.1 557 35.6% 452 81.1% 1.1Queens 11693 1,672 498 29.8% 33 2.0% 19 57.6% 1.9 344 20.6% 143 41.6% 1.4Queens 11694 3,486 226 6.5% 15 0.4% 3 20.0% 3.1 361 10.4% 46 12.7% 2.0Queens 11697 93 2 2.2% 0 0.0% 0 N/A 0 0.0% 0 N/A

County Zip!Code














Richmond TOTALS 102,418 14,728 14.4% 1,677 1.6% 454 27.1% 1.9 10,534 10.3% 3058 29.0% 2.0Richmond 10301 5,821 1,572 27.0% 116 2.0% 62 53.4% 2.0 576 9.9% 293 50.9% 1.9Richmond 10302 3,211 1,130 35.2% 103 3.2% 51 49.5% 1.4 665 20.7% 364 54.7% 1.6Richmond 10303 4,590 2,071 45.1% 173 3.8% 101 58.4% 1.3 1,258 27.4% 698 55.5% 1.2Richmond 10304 7,295 2,333 32.0% 154 2.1% 76 49.4% 1.5 926 12.7% 524 56.6% 1.8Richmond 10305 8,524 968 11.4% 129 1.5% 19 14.7% 1.3 729 8.6% 142 19.5% 1.7Richmond 10306 13,733 1,094 8.0% 160 1.2% 12 7.5% 0.9 1,087 7.9% 137 12.6% 1.6Richmond 10307 3,272 203 6.2% 51 1.6% 4 7.8% 1.3 257 7.9% 21 8.2% 1.3Richmond 10308 7,311 503 6.9% 93 1.3% 8 8.6% 1.3 584 8.0% 61 10.4% 1.5Richmond 10309 7,759 550 7.1% 127 1.6% 10 7.9% 1.1 557 7.2% 46 8.3% 1.2Richmond 10310 4,672 1,164 24.9% 90 1.9% 45 50.0% 2.0 699 15.0% 325 46.5% 1.9Richmond 10312 15,937 1,230 7.7% 216 1.4% 24 11.1% 1.4 1,183 7.4% 150 12.7% 1.6Richmond 10314 20,293 1,910 9.4% 265 1.3% 42 15.8% 1.7 2,013 9.9% 297 14.8% 1.6

County Zip!Code














Nassau TOTALS 336747 54046 16.0% N/A N/A N/A N/A 36146 10.7% 13396 37.1% 2.3Nassau 11001 6419 828 12.9% N/A N/A N/A N/A 566 8.8% 146 25.8% 2.0Nassau 11003 8655 4861 56.2% N/A N/A N/A N/A 1936 22.4% 1316 68.0% 1.2Nassau 11010 6338 637 10.1% N/A N/A N/A N/A 526 8.3% 116 22.1% 2.2Nassau 11020 1574 198 12.6% N/A N/A N/A N/A 65 4.1% 18 27.7% 2.2Nassau 11021 2939 95 3.2% N/A N/A N/A N/A 103 3.5% 1 1.0% 0.3Nassau 11023 2276 50 2.2% N/A N/A N/A N/A 87 3.8% 2 2.3% 1.0Nassau 11024 1842 35 1.9% N/A N/A N/A N/A 36 2.0% 1 2.8% 1.5Nassau 11030 5088 214 4.2% N/A N/A N/A N/A 136 2.7% 9 6.6% 1.6Nassau 11040 11218 953 8.5% N/A N/A N/A N/A 702 6.3% 133 18.9% 2.2Nassau 11050 6905 405 5.9% N/A N/A N/A N/A 319 4.6% 30 9.4% 1.6Nassau 11096 1251 578 46.2% N/A N/A N/A N/A 206 16.5% 140 68.0% 1.5Nassau 11501 4305 573 13.3% N/A N/A N/A N/A 265 6.2% 53 20.0% 1.5Nassau 11507 2183 126 5.8% N/A N/A N/A N/A 115 5.3% 13 11.3% 2.0Nassau 11509 1104 39 3.5% N/A N/A N/A N/A 45 4.1% 2 4.4% 1.3Nassau 11510 8632 2518 29.2% N/A N/A N/A N/A 1739 20.1% 749 43.1% 1.5Nassau 11514 1163 117 10.1% N/A N/A N/A N/A 93 8.0% 20 21.5% 2.1Nassau 11516 1548 81 5.2% N/A N/A N/A N/A 115 7.4% 4 3.5% 0.7Nassau 11518 2813 163 5.8% N/A N/A N/A N/A 260 9.2% 29 11.2% 1.9Nassau 11520 8060 4467 55.4% N/A N/A N/A N/A 1892 23.5% 1310 69.2% 1.2Nassau 11530 7796 339 4.3% N/A N/A N/A N/A 338 4.3% 19 5.6% 1.3Nassau 11542 5690 724 12.7% N/A N/A N/A N/A 406 7.1% 122 30.0% 2.4

County Zip!Code














Nassau 11545 3471 161 4.6% N/A N/A N/A N/A 141 4.1% 9 6.4% 1.4Nassau 11547 310 17 5.5% N/A N/A N/A N/A 18 5.8% 1 5.6% 1.0Nassau 11548 348 24 6.9% N/A N/A N/A N/A 20 5.7% 1 5.0% 0.7Nassau 11550 7542 6153 81.6% N/A N/A N/A N/A 2312 30.7% 2057 89.0% 1.1Nassau 11552 6411 1606 25.1% N/A N/A N/A N/A 922 14.4% 416 45.1% 1.8Nassau 11553 4955 4230 85.4% N/A N/A N/A N/A 1238 25.0% 1150 92.9% 1.1Nassau 11554 9631 959 10.0% N/A N/A N/A N/A 1063 11.0% 255 24.0% 2.4Nassau 11557 2088 109 5.2% N/A N/A N/A N/A 138 6.6% 16 11.6% 2.2Nassau 11558 2332 300 12.9% N/A N/A N/A N/A 235 10.1% 63 26.8% 2.1Nassau 11559 1896 102 5.4% N/A N/A N/A N/A 113 6.0% 9 8.0% 1.5Nassau 11560 1898 122 6.4% N/A N/A N/A N/A 102 5.4% 11 10.8% 1.7Nassau 11561 8902 746 8.4% N/A N/A N/A N/A 587 6.6% 91 15.5% 1.8Nassau 11563 5476 526 9.6% N/A N/A N/A N/A 570 10.4% 115 20.2% 2.1Nassau 11565 2904 242 8.3% N/A N/A N/A N/A 263 9.1% 46 17.5% 2.1Nassau 11566 9742 546 5.6% N/A N/A N/A N/A 834 8.6% 93 11.2% 2.0Nassau 11568 902 43 4.8% N/A N/A N/A N/A 22 2.4% 2 9.1% 1.9Nassau 11569 682 10 1.5% N/A N/A N/A N/A 18 2.6% 0 0.0%Nassau 11570 6336 689 10.9% N/A N/A N/A N/A 439 6.9% 109 24.8% 2.3Nassau 11572 8780 628 7.2% N/A N/A N/A N/A 856 9.7% 113 13.2% 1.8Nassau 11575 3378 3116 92.2% N/A N/A N/A N/A 1135 33.6% 1076 94.8% 1.0Nassau 11576 3693 120 3.2% N/A N/A N/A N/A 134 3.6% 8 6.0% 1.8Nassau 11577 3182 152 4.8% N/A N/A N/A N/A 213 6.7% 15 7.0% 1.5Nassau 11579 1528 75 4.9% N/A N/A N/A N/A 95 6.2% 2 2.1% 0.4

County Zip!Code














Nassau 11580 9794 2603 26.6% N/A N/A N/A N/A 1837 18.8% 804 43.8% 1.6Nassau 11581 5549 718 12.9% N/A N/A N/A N/A 705 12.7% 198 28.1% 2.2Nassau 11590 10293 3975 38.6% N/A N/A N/A N/A 1611 15.7% 980 60.8% 1.6Nassau 11596 2876 153 5.3% N/A N/A N/A N/A 138 4.8% 11 8.0% 1.5Nassau 11598 3420 122 3.6% N/A N/A N/A N/A 242 7.1% 13 5.4% 1.5Nassau 11709 2125 115 5.4% N/A N/A N/A N/A 144 6.8% 7 4.9% 0.9Nassau 11710 9405 582 6.2% N/A N/A N/A N/A 935 9.9% 92 9.8% 1.6Nassau 11714 6156 404 6.6% N/A N/A N/A N/A 606 9.8% 166 27.4% 4.2Nassau 11732 947 46 4.9% N/A N/A N/A N/A 45 4.8% 4 8.9% 1.8Nassau 11735 8002 892 11.1% N/A N/A N/A N/A 817 10.2% 141 17.3% 1.5Nassau 11753 3539 117 3.3% N/A N/A N/A N/A 181 5.1% 10 5.5% 1.7Nassau 11756 12286 1286 10.5% N/A N/A N/A N/A 1962 16.0% 416 21.2% 2.0Nassau 11758 15652 1587 10.1% N/A N/A N/A N/A 1328 8.5% 284 21.4% 2.1Nassau 11762 6583 331 5.0% N/A N/A N/A N/A 453 6.9% 32 7.1% 1.4Nassau 11765 204 8 3.9% N/A N/A N/A N/A 8 3.9% 0 0.0% 0.0Nassau 11771 2526 167 6.6% N/A N/A N/A N/A 130 5.1% 18 13.8% 2.1Nassau 11783 6153 348 5.7% N/A N/A N/A N/A 584 9.5% 54 9.2% 1.6Nassau 11791 7290 277 3.8% N/A N/A N/A N/A 390 5.3% 18 4.6% 1.2Nassau 11793 9215 524 5.7% N/A N/A N/A N/A 801 8.7% 84 10.5% 1.8Nassau 11797 2225 70 3.1% N/A N/A N/A N/A 79 3.6% 0 0.0%Nassau 11801 10919 1109 10.2% N/A N/A N/A N/A 1232 11.3% 269 21.8% 2.1Nassau 11803 8715 373 4.3% N/A N/A N/A N/A 597 6.9% 39 6.5% 1.5Nassau 11804 1494 46 3.1% N/A N/A N/A N/A 88 5.9% 4 4.5% 1.5

County Zip!Code














Suffolk TOTALS 369755 48551 13.1% 7101 1.9% 2178 30.7% 2.3 8385 2.3% 2430 29.0% 2.2Suffolk 11701 5376 2477 46.1% 155 2.9% 115 74.2% 1.6 147 2.7% 112 76.2% 1.7Suffolk 11702 3887 222 5.7% 32 0.8% 4 12.5% 2.2 40 1.0% 3 7.5% 1.3Suffolk 11703 4258 481 11.3% 72 1.7% 14 19.4% 1.7 98 2.3% 29 29.6% 2.6Suffolk 11704 8624 1691 19.6% 203 2.4% 76 37.4% 1.9 220 2.6% 69 31.4% 1.6Suffolk 11705 2177 80 3.7% 26 1.2% 0 0.0% 0.0 16 0.7% 0 0.0%Suffolk 11706 12849 3868 30.1% 403 3.1% 204 50.6% 1.7 384 3.0% 203 52.9% 1.8Suffolk 11713 2316 676 29.2% 64 2.8% 31 48.4% 1.7 86 3.7% 51 59.3% 2.0Suffolk 11715 1428 54 3.8% 19 1.3% 2 10.5% 2.8 21 1.5% 0 0.0%Suffolk 11716 2621 130 5.0% 30 1.1% 1 3.3% 0.7 21 0.8% 3 14.3% 2.9Suffolk 11717 9557 5949 62.2% 481 5.0% 362 75.3% 1.2 542 5.7% 409 75.5% 1.2Suffolk 11718 979 41 4.2% 8 0.8% 1 12.5% 3.0 9 0.9% 0 0.0%Suffolk 11719 963 129 13.4% 15 1.6% 4 26.7% 2.0 14 1.5% 3 21.4% 1.6Suffolk 11720 7225 678 9.4% 132 1.8% 19 14.4% 1.5 209 2.9% 39 18.7% 2.0Suffolk 11721 2095 80 3.8% 13 0.6% 0 0.0% 0.0 18 0.9% 0 0.0%Suffolk 11722 6767 3414 50.5% 334 4.9% 218 65.3% 1.3 361 5.3% 248 68.7% 1.4Suffolk 11724 904 24 2.7% 2 0.2% 0 0.0% 0.0 5 0.6% 1 20.0% 7.5Suffolk 11725 8114 429 5.3% 67 0.8% 4 6.0% 1.1 88 1.1% 9 10.2% 1.9Suffolk 11726 4046 1321 32.6% 134 3.3% 72 53.7% 1.6 108 2.7% 70 64.8% 2.0Suffolk 11727 6577 940 14.3% 157 2.4% 47 29.9% 2.1 221 3.4% 49 22.2% 1.6Suffolk 11729 7097 1462 20.6% 141 2.0% 41 29.1% 1.4 174 2.5% 60 34.5% 1.7Suffolk 11730 3894 197 5.1% 40 1.0% 4 10.0% 2.0 54 1.4% 5 9.3% 1.8Suffolk 11731 8658 384 4.4% 91 1.1% 4 4.4% 1.0 115 1.3% 9 7.8% 1.8

County Zip!Code














Suffolk 11733 5146 241 4.7% 38 0.7% 5 13.2% 2.8 76 1.5% 4 5.3% 1.1Suffolk 11738 3967 386 9.7% 96 2.4% 14 14.6% 1.5 105 2.6% 16 15.2% 1.6Suffolk 11739 400 17 4.3% 2 0.5% 0 0.0% 4 1.0% 0 0.0%Suffolk 11740 2591 198 7.6% 21 0.8% 4 19.0% 2.5 44 1.7% 9 20.5% 2.7Suffolk 11741 7112 537 7.6% 76 1.1% 8 10.5% 1.4 143 2.0% 20 14.0% 1.9Suffolk 11742 3280 265 8.1% 51 1.6% 6 11.8% 1.5 48 1.5% 4 8.3% 1.0Suffolk 11743 12573 1045 8.3% 138 1.1% 29 21.0% 2.5 124 1.0% 32 25.8% 3.1Suffolk 11746 16760 1875 11.2% 288 1.7% 88 30.6% 2.7 288 1.7% 88 30.6% 2.7Suffolk 11747 3929 200 5.1% 37 0.9% 4 10.8% 2.1 34 0.9% 4 11.8% 2.3Suffolk 11749 921 190 20.6% 23 2.5% 12 52.2% 2.5 37 4.0% 19 51.4% 2.5Suffolk 11751 3683 255 6.9% 57 1.5% 12 21.1% 3.0 80 2.2% 9 11.3% 1.6Suffolk 11752 2571 244 9.5% 50 1.9% 7 14.0% 1.5 54 2.1% 7 13.0% 1.4Suffolk 11754 5081 239 4.7% 45 0.9% 1 2.2% 0.5 47 0.9% 5 10.6% 2.3Suffolk 11755 3297 209 6.3% 46 1.4% 5 10.9% 1.7 66 2.0% 4 6.1% 1.0Suffolk 11757 11005 1032 9.4% 223 2.0% 44 19.7% 2.1 270 2.5% 47 17.4% 1.9Suffolk 11763 7511 1465 19.5% 199 2.6% 71 35.7% 1.8 259 3.4% 88 34.0% 1.7Suffolk 11764 3692 198 5.4% 64 1.7% 4 6.3% 1.2 56 1.5% 6 10.7% 2.0Suffolk 11766 3777 228 6.0% 60 1.6% 10 16.7% 2.8 62 1.6% 5 8.1% 1.3Suffolk 11767 3975 194 4.9% 46 1.2% 3 6.5% 1.3 57 1.4% 5 8.8% 1.8Suffolk 11768 6537 235 3.6% 51 0.8% 5 9.8% 2.7 48 0.7% 1 2.1% 0.6Suffolk 11769 2591 133 5.1% 22 0.8% 1 4.5% 0.9 33 1.3% 3 9.1% 1.8Suffolk 11772 9868 1408 14.3% 279 2.8% 101 36.2% 2.5 303 3.1% 93 30.7% 2.2Suffolk 11776 5991 640 10.7% 100 1.7% 18 18.0% 1.7 172 2.9% 36 20.9% 2.0Suffolk 11777 2587 95 3.7% 19 0.7% 2 10.5% 2.9 28 1.1% 1 3.6% 1.0

County Zip!Code














Suffolk 11778 3605 229 6.4% 93 2.6% 9 9.7% 1.5 133 3.7% 11 8.3% 1.3Suffolk 11779 9810 779 7.9% 190 1.9% 25 13.2% 1.7 237 2.4% 30 12.7% 1.6Suffolk 11780 4455 182 4.1% 47 1.1% 4 8.5% 2.1 59 1.3% 4 6.8% 1.7Suffolk 11782 4269 160 3.7% 47 1.1% 1 2.1% 0.6 53 1.2% 4 7.5% 2.0Suffolk 11784 6025 657 10.9% 151 2.5% 34 22.5% 2.1 227 3.8% 37 16.3% 1.5Suffolk 11786 1837 97 5.3% 31 1.7% 6 19.4% 3.7 22 1.2% 2 9.1% 1.7Suffolk 11787 9677 476 4.9% 76 0.8% 10 13.2% 2.7 105 1.1% 8 7.6% 1.5Suffolk 11788 4215 245 5.8% 33 0.8% 5 15.2% 2.6 42 1.0% 3 7.1% 1.2Suffolk 11789 2292 119 5.2% 41 1.8% 3 7.3% 1.4 98 4.3% 6 6.1% 1.2Suffolk 11790 4044 170 4.2% 33 0.8% 1 3.0% 0.7 54 1.3% 3 5.6% 1.3Suffolk 11792 2568 128 5.0% 36 1.4% 0 0.0% 49 1.9% 7 14.3% 2.9Suffolk 11795 7416 407 5.5% 102 1.4% 10 9.8% 1.8 66 0.9% 3 4.5% 0.8Suffolk 11796 987 43 4.4% 5 0.5% 1 20.0% 4.6 13 1.3% 0 0.0%Suffolk 11798 2859 2398 83.9% 143 5.0% 119 83.2% 1.0 149 5.2% 148 99.3% 1.2Suffolk 11901 6604 1148 17.4% 92 1.4% 33 35.9% 2.1 81 1.2% 27 33.3% 1.9Suffolk 11933 2147 205 9.5% 38 1.8% 7 18.4% 1.9 33 1.5% 8 24.2% 2.5Suffolk 11934 2319 151 6.5% 39 1.7% 8 20.5% 3.2 37 1.6% 2 5.4% 0.8Suffolk 11935 1246 41 3.3% 7 0.6% 0 0.0% 12 1.0% 0 0.0%Suffolk 11937 405 27 6.7% 3 0.7% 0 0.0% 4 1.0% 0 0.0%Suffolk 11939 343 5 1.5% 2 0.6% 0 0.0% 5 1.5% 0 0.0%Suffolk 11940 1427 42 2.9% 20 1.4% 2 10.0% 3.4 24 1.7% 1 4.2% 1.4Suffolk 11941 714 28 3.9% 12 1.7% 0 0.0% 10 1.4% 1 10.0% 2.6Suffolk 11942 1605 84 5.2% 23 1.4% 1 4.3% 0.8 5 0.3% 2 40.0% 7.6Suffolk 11944 970 91 9.4% 6 0.6% 1 16.7% 1.8 10 1.0% 3 30.0% 3.2

County Zip!Code














Suffolk 11946 3910 332 8.5% 71 1.8% 24 33.8% 4.0 32 0.8% 13 40.6% 4.8Suffolk 11948 411 12 2.9% 6 1.5% 0 0.0% 6 1.5% 1 16.7% 5.7Suffolk 11949 4089 250 6.1% 66 1.6% 8 12.1% 2.0 69 1.7% 12 17.4% 2.8Suffolk 11950 3953 623 15.8% 189 4.8% 53 28.0% 1.8 239 6.0% 49 20.5% 1.3Suffolk 11951 3844 431 11.2% 166 4.3% 21 12.7% 1.1 303 7.9% 41 13.5% 1.2Suffolk 11952 1519 44 2.9% 10 0.7% 1 10.0% 3.5 7 0.5% 0 0.0%Suffolk 11953 3489 563 16.1% 100 2.9% 34 34.0% 2.1 144 4.1% 35 24.3% 1.5Suffolk 11955 672 19 2.8% 15 2.2% 0 0.0% 5 0.7% 1 20.0% 7.1Suffolk 11961 4819 225 4.7% 67 1.4% 5 7.5% 1.6 142 2.9% 9 6.3% 1.4Suffolk 11963 1886 59 3.1% 17 0.9% 2 11.8% 3.8 6 0.3% 0 0.0%Suffolk 11964 756 11 1.5% 8 1.1% 0 0.0% 2 0.3% 0 0.0%Suffolk 11967 6906 905 13.1% 307 4.4% 49 16.0% 1.2 415 6.0% 73 17.6% 1.3Suffolk 11968 3558 271 7.6% 59 1.7% 8 13.6% 1.8 10 0.3% 2 20.0% 2.6Suffolk 11971 2045 50 2.4% 17 0.8% 1 5.9% 2.4 17 0.8% 0 0.0%Suffolk 11976 592 81 13.7% 2 0.3% 0 0.0% 0 0.0% 0 N/ASuffolk 11977 748 25 3.3% 9 1.2% 2 22.2% 6.6 5 0.7% 1 20.0% 6.0Suffolk 11978 1159 77 6.6% 19 1.6% 5 26.3% 4.0 7 0.6% 1 14.3% 2.2Suffolk 11980 1238 76 6.1% 22 1.8% 2 9.1% 1.5 34 2.7% 7 20.6% 3.4

County Zip!Code














Westchester TOTALS 184777 28901 15.6% N/A N/A ` N/A 15528 8.4% 5194 33.4% 2.1Westchester 10501 338 18 5.3% N/A N/A N/A N/A 10 3.0% 0 0.0%Westchester 10502 1646 112 6.8% N/A N/A N/A N/A 110 6.7% 8 7.3% 1.1Westchester 10504 2266 76 3.4% N/A N/A N/A N/A 75 3.3% 4 5.3% 1.6Westchester 10504 2266 76 3.4% N/A N/A N/A N/A 75 3.3% 4 5.3% 1.6Westchester 10505 228 19 8.3% N/A N/A N/A N/A 20 8.8% 3 15.0% 1.8Westchester 10506 1690 69 4.1% N/A N/A N/A N/A 68 4.0% 6 8.8% 2.2Westchester 10507 1088 117 10.8% N/A N/A N/A N/A 82 7.5% 22 26.8% 2.5Westchester 10510 2798 120 4.3% N/A N/A N/A N/A 161 5.8% 10 6.2% 1.4Westchester 10511 604 57 9.4% N/A N/A N/A N/A 81 13.4% 13 16.0% 1.7Westchester 10514 3394 109 3.2% N/A N/A N/A N/A 183 5.4% 7 3.8% 1.2Westchester 10517 147 11 7.5% N/A N/A N/A N/A 17 11.6% 1 5.9% 0.8Westchester 10518 412 11 2.7% N/A N/A N/A N/A 35 8.5% 1 2.9% 1.1Westchester 10520 3152 216 6.9% N/A N/A N/A N/A 221 7.0% 19 8.6% 1.3Westchester 10522 2102 115 5.5% N/A N/A N/A N/A 119 5.7% 9 7.6% 1.4Westchester 10523 1573 801 50.9% N/A N/A N/A N/A 230 14.6% 153 66.5% 1.3Westchester 10526 474 23 4.9% N/A N/A N/A N/A 46 9.7% 4 8.7% 1.8Westchester 10527 251 8 3.2% N/A N/A N/A N/A 23 9.2% 4 17.4% 5.5Westchester 10528 2470 141 5.7% N/A N/A N/A N/A 101 4.1% 9 8.9% 1.6Westchester 10530 3110 276 8.9% N/A N/A N/A N/A 227 7.3% 38 16.7% 1.9Westchester 10532 1463 112 7.7% N/A N/A N/A N/A 99 6.8% 12 12.1% 1.6Westchester 10533 1766 78 4.4% N/A N/A N/A N/A 78 4.4% 5 6.4% 1.5Westchester 10535 110 13 11.8% N/A N/A N/A N/A 12 10.9% 3 25.0% 2.1Westchester 10536 2936 102 3.5% N/A N/A N/A N/A 186 6.3% 11 5.9% 1.7

County Zip!Code














Westchester 10538 4110 208 5.1% N/A N/A N/A N/A 160 3.9% 12 7.5% 1.5Westchester 10543 3991 450 11.3% N/A N/A N/A N/A 243 6.1% 47 19.3% 1.7Westchester 10546 399 15 3.8% N/A N/A N/A N/A 35 8.8% 0 0.0%Westchester 10547 1770 243 13.7% N/A N/A N/A N/A 224 12.7% 50 22.3% 1.6Westchester 10548 898 70 7.8% N/A N/A N/A N/A 67 7.5% 15 22.4% 2.9Westchester 10549 3478 267 7.7% N/A N/A N/A N/A 158 4.5% 27 17.1% 2.2Westchester 10550 3415 2473 72.4% N/A N/A N/A N/A 962 28.2% 760 79.0% 1.1Westchester 10552 2680 1044 39.0% N/A N/A N/A N/A 298 11.1% 152 51.0% 1.3Westchester 10553 1673 1453 86.8% N/A N/A N/A N/A 496 29.6% 454 91.5% 1.1Westchester 10560 1343 69 5.1% N/A N/A N/A N/A 83 6.2% 5 6.0% 1.2Westchester 10562 6276 1225 19.5% N/A N/A N/A N/A 682 10.9% 243 35.6% 1.8Westchester 10566 4569 1323 29.0% N/A N/A N/A N/A 858 18.8% 346 40.3% 1.4Westchester 10567 5741 707 12.3% N/A N/A N/A N/A 671 11.7% 152 22.7% 1.8Westchester 10570 3457 220 6.4% N/A N/A N/A N/A 177 5.1% 10 5.6% 0.9Westchester 10573 6708 1510 22.5% N/A N/A N/A N/A 600 8.9% 260 43.3% 1.9Westchester 10576 1647 65 3.9% N/A N/A N/A N/A 99 6.0% 7 7.1% 1.8Westchester 10577 700 30 4.3% N/A N/A N/A N/A 14 2.0% 0 0.0%Westchester 10578 217 11 5.1% N/A N/A N/A N/A 14 6.5% 1 7.1% 1.4Westchester 10580 3945 177 4.5% N/A N/A N/A N/A 103 2.6% 5 4.9% 1.1Westchester 10583 9618 376 3.9% N/A N/A N/A N/A 348 3.6% 12 3.4% 0.9Westchester 10587 64 6 9.4% N/A N/A N/A N/A 5 7.8% 2 40.0% 4.3Westchester 10588 603 58 9.6% N/A N/A N/A N/A 69 11.4% 9 13.0% 1.4Westchester 10589 3436 137 4.0% N/A N/A N/A N/A 213 6.2% 7 3.3% 0.8Westchester 10590 2126 105 4.9% N/A N/A N/A N/A 180 8.5% 12 6.7% 1.3

County Zip!Code














Westchester 10591 4045 566 14.0% N/A N/A N/A N/A 215 5.3% 58 27.0% 1.9Westchester 10594 1400 94 6.7% N/A N/A N/A N/A 58 4.1% 4 6.9% 1.0Westchester 10595 1597 114 7.1% N/A N/A N/A N/A 95 5.9% 6 6.3% 0.9Westchester 10596 303 24 7.9% N/A N/A N/A N/A 44 14.5% 5 11.4% 1.4Westchester 10597 282 9 3.2% N/A N/A N/A N/A 15 5.3% 0 0.0%Westchester 10598 8936 648 7.3% N/A N/A N/A N/A 865 9.7% 119 13.8% 1.9Westchester 10601 978 152 15.5% N/A N/A N/A N/A 35 3.6% 7 20.0% 1.3Westchester 10603 3751 1532 40.8% N/A N/A N/A N/A 374 10.0% 215 57.5% 1.4Westchester 10604 2035 244 12.0% N/A N/A N/A N/A 123 6.0% 28 22.8% 1.9Westchester 10605 4536 414 9.1% N/A N/A N/A N/A 275 6.1% 50 18.2% 2.0Westchester 10606 2336 714 30.6% N/A N/A N/A N/A 225 9.6% 104 46.2% 1.5Westchester 10607 2027 1022 50.4% N/A N/A N/A N/A 268 13.2% 186 69.4% 1.4Westchester 10701 5159 1611 31.2% N/A N/A N/A N/A 702 13.6% 366 52.1% 1.7Westchester 10703 2871 687 23.9% N/A N/A N/A N/A 408 14.2% 183 44.9% 1.9Westchester 10704 4831 634 13.1% N/A N/A N/A N/A 444 9.2% 125 28.2% 2.1Westchester 10705 2944 840 28.5% N/A N/A N/A N/A 435 14.8% 199 45.7% 1.6Westchester 10706 2113 94 4.4% N/A N/A N/A N/A 88 4.2% 6 6.8% 1.5Westchester 10707 2264 236 10.4% N/A N/A N/A N/A 120 5.3% 29 24.2% 2.3Westchester 10708 3565 274 7.7% N/A N/A N/A N/A 153 4.3% 14 9.2% 1.2Westchester 10709 2141 140 6.5% N/A N/A N/A N/A 96 4.5% 5 5.2% 0.8Westchester 10710 5255 910 17.3% N/A N/A N/A N/A 406 7.7% 128 31.5% 1.8Westchester 10801 5013 2148 42.8% N/A N/A N/A N/A 571 11.4% 322 56.4% 1.3Westchester 10803 2993 263 8.8% N/A N/A N/A N/A 155 5.2% 32 20.6% 2.3Westchester 10804 4185 268 6.4% N/A N/A N/A N/A 239 5.7% 28 11.7% 1.8

County Zip!Code














Westchester 10805 1693 393 23.2% N/A N/A N/A N/A 124 7.3% 49 39.5% 1.7

Methodology The zip code based numbers in this study came from Listsource and its method of putting in criteria and getting a count of the number of households with those criteria in a specified geographic area for free. Through Listsource only owner-occupied houses were counted. All of the figures were obtained on between December 9 and December 15, 2010. Any zip code with fewer than 50 owner-occupied houses has been omitted for purposes of this report. Zip code level household income was obtained from the PolicyMap database.

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