Neighborhood Dynamics

Problem Description

The recent proliferation of energy disclosure policies in U.S. and global cities has begun to generate significant new streams of data on patterns of energy and water consumption in buildings (Burr, Keicher, and Lawrence 2013). The logic behind these policies is predicated on the power of measurement and information to shift awareness and market behavior around resource consumption and thus generate greater demand for more efficient, or “green”, properties (Kontokosta 2013, 2014). One of the first and most ambitious of these policies is New York City’s Local Law 84 (LL84), adopted as part of the City’s Greener, Greater Buildings Plan, in 2009. Green buildings have become an increasing focus of energy reduction and carbon reduction targets. In New York, buildings account for 94% of electricity use, 75% of greenhouse gas emissions, and 85% of potable water use (City of New York 2011).

As a major component of New York City’s long-term sustainability strategy, LL84 stipulates that all buildings of 50,000 square feet or more must report energy and (in certain cases) water consumption on an annual basis. In 2013, a total 10,548 multi-family buildings, accounting for over 1.2 billion square feet, reported energy use data as stipulated by LL84. Of these, 3,906 also reported water consumption data. The data used in this analysis represent an unprecedented sample of actual energy and water consumption, coupled with specific building, parcel, and neighborhood characteristics. In addition to energy and water data reported through LL84, building characteristics such as number of units, number of bedrooms, number of washer/dryers, and other physical and occupancy attributes are also collected (City of New York 2012, 2013; Kontokosta 2012, 2015; Kontokosta and Jain 2015).

In addition to environmental sustainability goals, energy and water use in multi-family housing have important implications for housing affordability, infrastructure planning, and other quality of life indicators. Energy costs can be a significant portion of lower-income households’ gross income (see Figure 1, below), and operating efficiencies improvement in multi-family buildings can yield meaningful savings (Block et al. 2012). Non-monetary costs, such as air pollution and associated public health issues caused by inefficient buildings and/or buildings burning heavy fuel oils, can also accrue disproportionately to economically distressed communities (City of New York 2011).

While the relative cost of potable water to the consumer is low, water use patterns and building water use intensity may be indicators of management issues and deferred maintenance. From an urban infrastructure perspective, water use can have significant impacts on environmental quality. In New York, for instance, the combined sewer overflow system sends untreated wastewater into adjacent waterways during periods of heavy rain or snow (NYC Department of Environmental Protection 2012).

Previous empirical analysis (Kontokosta, forthcoming) indicates that income, persons per housing unit, building size, building age, and unit amenities are among the statistically significant variables in explaining both water and energy use differentials. In a particularly important finding, subsidized housing is found, controlling for other building and occupancy characteristics, to have both higher energy and water consumption per square foot than market-rate buildings. This suggests that lower- income households may be disproportionately impacted by inefficient buildings and that subsidized housing presents opportunities for building retrofitting and efficiency strategies. These preliminary findings may provide important insights for urban policy relating both to sustainability and resource efficiency goals in multi-family housing and for targeting and incentivizing operational improvements in the subsidized housing stock.

The Challenge

Your challenge is to use the data provided (LL84, PLUTO, and SHIP) to examine energy and water use differentials across neighborhoods and socioeconomic groups. You are tasked with addressing fundamental questions of how energy and water use varies by demographic segment, income group, and neighborhood. Ultimately, you and your team should produce recommendations for further research based on your initial analysis, and identify neighborhood-level discrepancies in energy and water consumption in affordable and market-rate housing.

Provided Data

A readme file with links to relevant data, as well as the MapPLUTO data can be found here.