Redlining the LES: How Discriminatory Lending Policies Solidified Pre-Existing Inequalities

Tenement Museum building (1863) formerly home to an estimated 15K immigrants

The following was written by Anna Sargeantson. All photos courtesy of Anna Sargeantson, except where otherwise noted.

Home to dimly lit restaurants and bars frequented by NYU students and young professionals, the Lower East Side remains in many ways the epitome of New York City’s youthful heart. Under the surface, though, the neighborhood has historically suffered from a higher poverty rate as compared to neighboring pockets of Manhattan.

In 2019, the median household income in the Lower East Side and Chinatown was $42,010, according to NYU’s Furman Center for Real Estate and Urban Policy. That’s 40% less than the recorded citywide figure of $70,590. Similarly, the poverty rate among the LES and Chinatown was documented at 24% in 2019, 8 percentage points above the rate across Manhattan.

The current financial landscape of the LES can be traced to immigration patterns dating back to the 1800s, and the consequent lending practices endemic to redlining starting in the 1930s. With this, trends in median household income in the Lower East Side are a direct reflection of discriminatory policies sanctioned by the government.

In order to conceptualize the implications of redlining, we must look at historical trends in the neighborhood’s demographic. Since the early 1800s, the LES has been an epicenter of immigration. The community has hosted diverse German, Jewish, Italian, Irish, Polish, Ukrainian, Puerto Rican, and Chinese populations. Geopolitical events promulgating instability and financial hardship such as the Potato Famine in Ireland and rampant antisemitism in Europe drove migration to New York City across the 19th and 20th centuries. As small cultural communities formed, more and more immigrants became concentrated in the population-dense Lower East Side.

In the mid-1800s, New York City’s population boomed. In response, low-rent tenement housing was constructed to house immigrants more efficiently. These multi-family homes were characterized by overcrowding and general health and safety violations. The conditions left tenants susceptible to fast-spreading illnesses and disastrous fires. One can imagine that with low paying jobs largely in the garment production industry and harsh living conditions at home, it was difficult for immigrants to break out of poverty and advance a better life for themselves. Redlining only made matters worse.

In the 1930s, the Federal Housing Administration (FHA) launched redlining as a policy under Franklin D. Roosevelt’s New Deal. The FHA drew maps of specific districts in major U.S. cities considered to be risky investments in order to guide their lending policies. At its core, the policy was leveraged to deny lending to individuals and communities deemed most likely to default on their loans.

Depiction of redlining in Manhattan, Photo: The University of Richmond Mapping Inequality initiative

In 2016, the University of Richmond launched an initiative called “Mapping Inequality,” an interactive map that visualizes redlining across the U.S. In mapping districts of major cities, the Home Owners’ Loan Corporation (HOLC) constructed a scale from A-D which codified the reliability of investment. On the scale, A signified the lowest risk, while D alerted to high-risk areas, marked in red. The area from the East Village into the Lower East Side is clearly codified as problematic.

Scholars have identified a whole host of consequences of redlining, some more obscure than others. For example, environmental justice experts have even correlated redlined districts with increased pollution, as waste transfer stations and other industrial plants which were generally placed in low-income, redlined pockets of American cities. Of course, increased emissions in concentrated areas would have downstream health impacts on local communities.

Government decisions surrounding eligibility for loans were inherently rooted in race as they took into account the demographic of the residents in each region of the city. Unsurprisingly, Code D regions across the nation were directly correlated with those where the population was majority Black and Latino. In the case of the LES, however, risky investment corresponded to where immigrants tended to reside.