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A Finance Map of NYC:  Reducing Carbon and Driving Large Scale Energy Efficiency with a Public Database To Support PACE Lending

 

Background

The Center for Sustainable Business (CSB) from New York University (NYU) Stern Business School is supporting the commitment of New York City (NYC) to limit carbon emissions under the Paris Agreement, collaborating closely with the NYC Mayor’s Office of Sustainability (MOS) and the NYC Clean Energy Efficiency Corp. (NYCEEC) to bring low cost, long term financing (PACE) for energy efficiency retrofits and renewable energy to buildings. To achieve NYC carbon neutrality by 2050, Invest NYC SDG, a two-year Stern CSB initiative, wants to guide priorities and outreach to building owners and lenders by creating a public database with visual interface mapping to present GHG emissions, potential penalties, and open liens of almost 40,000 buildings.
As the CUSP group, this project aims to help Invest NYC SDG to implement a website available to the public and develop an interactive map of NYC that displays CO2e emission level by building, contact details for the building owner or managing agent, and a list of lien holders. We expect this critical tool will empower driving energy efficiency in NYC buildings and deliver a completed user-friendly database that will be a valuable tool for all New Yorkers in the drive to reduce carbon emissions in NYC in the face of climate change.

 

Introduction

Invest NYC SDG is working with the Mayor’s Office of Sustainability to create a database containing the information necessary to calculate the fines imposed on buildings subject to LL 97. Specifically, the database contains the imputed CO2e of each energy stream, total CO2e, floorspace, usage, and characteristics that may qualify the building for special treatment under LL97. The database also contains the address and contact details of the owner or managing agent and the identity of any lien holders.

Invest NYC SDG is supported by NYU students, alumni and faculty that have assisted in assembling the data. The database will be hosted by the Furman Center at NYU and be available to the public. The motivation for creating the database is to inform outreach to property owners and lien holders. The database will be useful to the NYC Accelerator and members of the public interested in the CO2e of buildings in their neighborhood.

Invest NYC SDG intends to create an online application that displays the results of queries run against the database. 

The CUSP Capstone project would be to help Invest NYC SDG develop an interactive map of NYC that displays:

1. CO2e emission level by building, contact details for the building owner or managing

agent and a list of lien holders.

2. the results of a series of queries -- either run in advance or possibly user defined,such as the true ownership of NYC buildings.

 
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Problem Statement

 

Current issues

The general problem of CUSP group is how to help Invest NYC SDG Initiative to support the implementation of a website available to the public and develop an interactive map of NYC that displays:

●  CO2e emission level by building, contact details for the building owner or managing agent, and a list of lien holders.

●  the results of a series of queries -- either run in advance or possibly user-defined.

Research Objective

  • Support the implementation of a website available to the public

  • Design an application that can be easily navigated by the user.

  • Determine the filters that should be used to display subsets of the data on a map.

  • Employ data visualization tools to create visual representations of large data sets that describe NYC buildings.

Research Questions

  • How to find the address that the user is searching, no matter the way it is written?

  • How to know the portfolio of buildings owned by the same owner?

  • How to determine the filters that should be used to display subsets of the data on a map?

  • How to employ data visualization tools to create visual representations of large data sets that describe NYC buildings?

Original Data

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Department of Building

FEATURED ITEM

BBL
Complaints
Violations
Jobs

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HPD Registration Contacts

CUSTOMER FAVORITE

Registration ContactID
Corporation Name

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Property Valuation and Assessment Data

NEW ADDITION

Tax information
Building types

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LL97/LL84 Database

FEATURED ITEM

Emissions
Energy consumption by source
Year built
Gross floor area
Energy use intensity

Learn More
 

Results

Fuzzy Matching

Fuzzy Matching is a technique used in computer-assisted translation as a special case of record linkage. It works with matches that may be less than 100% perfect when finding correspondences between segments of a text and entries in a database of previous translations.The concept of Fuzzy Matching is to calculate similarity between any two given strings. And this is achieved by making use of the Levenshtein Distance between the two strings. FuzzyWuzzy is a library of Python which is used for string matching. Fuzzy string matching is the process of finding strings that match a given pattern. FuzzyWuzzy has many powerful functions that allow us to deal with more complex situations such as substring matching .

We also tried to achieve Fuzzy Matching by Ruby. We used a geocoder gem to make a more educated guess by comparing the latitude and longitude of two addresses and returning true if they are close enough. It connects to more than 40 API worldwide so it is very powerful. We also used the address matcher library in Ruby to achieve our functions. Specifically, we first created an address library with all of the known address strings we wanted to match against, then called match to find the closest matching address. For instance, we can use our library to measure whether “1000 5th Ave, New York, NY 10028” and “1000 5th Avenue, New York City, NY 10028” is the same address 

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Building and Merging of Database

We compared the owners with the largest number of buildings in New York City mentioned in the ’Who owns all of New York’ article .  "No. buildings / vacant parts" is the data mentioned in the article, and "owner in our merged file" is our own data. We found that our own data is quite different from those mentioned in the article. After analysis, we think that we mainly use the real owner data in DOB data, while the data used in the article is only  the  business  name  or  company  name , so we have more data.  Moreover, our data  do not count the buildings of the whole NYC, so these two reasons lead to great differences.

Identify and Analyze LLC Ownership

We need to organize different databases and constantly increase information about owners. So based on the data of the Pluto website, according to the information of the BBL column and the registration information of the HPD data column, we used Python to combine the data of HPD and Acris website. Without changing the order of basic data, we added new information about owners, including gas emission information, geographic information, tax information, mailing information and housing use information.After we found the DOB database, we added the real owner information. Our database already has real owner information, but now the problem is that a BBL will correspond to many different owners. We are summarizing data through different owner types and business names.So then we use SQL to make classification and adjustment, and classify the same BBL according to the house type.

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Rank the Top Owner in NYC

We compared the owners with the largest number of buildings in New York City mentioned in the ’Who owns all of New York’ article . "No. buildings / vacant parts" is the data mentioned in the article, and "owner in our merged file" is our own data. We found that our own data is quite different from those mentioned in the article. After analysis, we think that we mainly use the real owner data in DOB data, while the data used in the article is only the business name or company name, so we have more data.  Moreover, our data  do not  count the buildings of the whole NYC, so these two reasons lead to great differences.

 
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Create the Tableau Dashboard  & Analyze Graphs

We created a Tableau dashboard to demonstrate total emissions by four key categories, as to find some insightful results. One graph is displayed by different construction dates, one is by different kinds of city owned BBL, one is plotted by different fuels, the last one is by low-income housing tax credit qualified census tracts. As for construction dates, we divided the construction period into four parts, 1749-1840, 1841-1920, 1921-1945, 1946-1990 and 1991 to now respectively.  It is apparent from our graph that most buildings (10,202) in NYC were constructed during 1921-1945 while only 4,580 buildings were built currently. We displayed them in a pie chart. As for city-owned buildings, we utilized the horizontal bar chart for demonstrating . From our graph, most buildings are not owned by cities. Among them, multifamily housing takes the largest percentage, reaching 24,020, and office makes up the second largest percent, with 2,265 number of BBL. Comparingly, K-12 schools account for the largest proportion of city-owned-buildings, with 1,582 BBL. In addition, for fuel part , we divided fuels into 9 categories, Fuel Oil #1 Use, Fuel Oil #2 Use, Diesel #2 Use, Kerosene, Natural Gas, and so on. Beyond that, we also displayed these kinds of fuels in 6 boroughs in NYC since we wanted to find the different performances of fuel consumptions in different boroughs. It is very clear that Manhattan consumes most fuels whereas Staten island consumes least, which is very reasonable considering the characteristics of the two boroughs. In all boroughs, natural gas use accounts for the largest proportion of total fuel use. Lastly, from our dashboard, 68.03% records are not low-income housing tax credit qualified census tracts, we used a pie chart to display it .

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NYC Buildings Map

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Conclusion

 

Summary

In this project, the group mainly worked on identifying LLC ownership and support Invest NYC SDG to implement the public data tool by providing the work of data visualizations. The data tool will inform building owners, managers, architects, engineers and vendors of energy
efficiency solutions of the CO2e produced by a property and the potential LL97 fines facing the property. The data will identify both the owner of the property and the holders of any mortgage liens on the property and, if a lien has been securitized, whether the securitization is a “private label” or a U.S. Agency securitization. The user interface allows the user to easily identify portfolios of buildings under common ownership, with common mortgage lenders, or that are located in the same neighborhood.
The data tool will also identify those properties covered by LL97 whose mortgages have are held by a CMBS (Commercial Mortgage Back Security) trustee and indicates whether the securitization is a U.S. Agency CMBS (Fannie Mae, Freddie Mac) or a “private label” CMBS. Private label CMBS trustees and/or servicers are unlikely to cede priority to a PACE financing. We estimate that there are more than 8,000 CMBS buildings -- nearly one third of LL97-covered buildings. This data can be used to show the total carbon produced by buildings where the mortgage is held in a CMBS and spearhead conversations to change the securitization documents and new provisions regarding PACE in workouts of distressed buildings.

Sponsor:NYU Stern Center for Sustainable Business

Sponsor Website:   https://www.stern.nyu.edu/experience-stern/about/departments-centers-

initiatives/centers-of-research/center-sustainable-business

Sponsor Description:

The NYU Stern Center for Sustainable Business is a key program within the NYU Stern School of Business and a trusted partner for business leaders who want to drive change and redesign capitalism to sustain both business and society for generations to come.

The Center was founded on the principle that sustainable business is good business; delivering better financial results while protecting the planet and its people. We aim to help current and future business leaders embrace proactive and innovative mainstreaming of sustainability,resulting in competitive advantage and resiliency for their companies as well as a positive impact for society.

Faculty Mentor:

Gary Friedland

Marianna Koval

Miles Draycott

 

Team Members

 
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Haiqiang An

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Huanran Kong

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Yuetong Zhou

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Yuanzhe Luo