Proportionalities: A Decade of Housing Markets to Subway Stations in Manhattan from 2006 to 2016 by Nick Kunz

proportionalities A Decade of Housing Markets to Subway Stations in Manhattan from 2006 to 2016 Columbia GSAPP Geographic Information Systems Nick Kunz 20171211

00 contents 01 02 03 04 05 06 07 08

introduction methodology concentrations distances proportionalities time postulation appendix

methodology

01 introduction

Roughly 55% of Manhattan is within walking distance to a subway station. It is often thought that high activity or â&#x20AC;&#x2DC;hotâ&#x20AC;&#x2122; housing markets in the borough are driven in part by proximity to them.

Was the percentage of the highest spatial concentrations of residential real estate market liquidity within walking distance to a subway station, higher than the percentage observed globally in Manhattan within the last decade from 2006 to 2016?

If walking distance to a subway station explains the highest spatial concentrations of residential real estate market liquidity, it would be reasonable to expect that they exhibit a greater or equal proportionality in that regard.

This investigation seeks to help answer that question by virtue of the following methodology.

02 methodology

Phase 1

Phase 2

Residential Real Estate Sales (Table)

Clean, Organize & Omit Commercial Real Estate, $0 Sales, Control for Years

Phase 3

Locations of Residential Real Estate Sales within the Last Decade (Points)

Geolocate by Address & Zip Code Utilizing Dual Range ‘Geocoder’ & Export

Calculate Spatial Concentration Utilizing ‘Kernel Density’ by Transactions 0.25 mi Radius Calculate Spatial Concentration Utilizing ‘Kernel Density’ by Price / Unit 0.25 mi Radius Calculate Spatial Concentration Utilizing ‘Kernel Density’ by Unit Count / Sale 0.25 mi Radius

Subway Station Locations (Points)

Street Network (Lines)

Green Space (Polygons)

Water (Polygons)

Borough Boundaries (Polygons)

Classify Kernel Densities by Standard Deviation Utilizing ‘Reclassify’ & Export

Verify Projection

Select by Location within Manhattan Boundary & Export

Verify Projection

Select by Location within Manhattan Boundary & Export

Verify Projection

Select by Location within Manhattan Boundary & Export

Verify Projection

Select by Location within Manhattan Boundary & Export

Interactively Select Manhattan & Export Verify Projection

Manhattan Boundary (Polygons)

Phase 4

Kernel Density Map by Transactions Ranked Decision Layer (Raster) 3A1

Kernel Density Map by Price / Unit Ranked Decision Layer (Raster) 3A2

Calculate Highest Ranking Spatial Concentrations of Liquidity by Conducting Map Algebra Utilizing ‘Raster Calculator’ & Export

Weighted Decision Map of the Highest Spatial Concentrations of Liquidity (Raster)

Kernel Density Map by Unit Count / Sale Ranked Decision Layer (Raster) 3A 3A3

Manhattan Subway Station Locations (Points)

Manhattan Street Network (Lines)

Manhattan Green Space (Polygons)

Manhattan Water (Polygons)

Calculate Network Distance & Generate Buffer Ring by 0.25 mi Utilizing ‘Network Analysis’ & Export

Walking Distance to Subway Stations (Polygons)

4BC

Base Map (Polygons)

4DE

Phase 5 Convert Raster to Polygon Feature Class Utilizing ‘Raster to Polygon’ Calculate Area Utilizing ‘Calculate Geometry’

Phase 6

Weighted Decision Map of the Highest Spatial Concentrations of Liquidity with Calculated Area (Polygons)

Determine Highest Spatial Concentration of Liquidity within Walking Distance to a Subway Station Utilizing ‘Clip’

Calculate Area Utilizing ‘Calculate Geometry’

Map of Manhattan within Walking Distance Subway Stations with Calculated Area (Polygons)

5BC

Calculate Area Utilizing ‘Calculate Geometry’

Map of the Highest Spatial Concentration of Liquidity within Walking Distance to Subway Stations with Calculated Area (Polygons)

Calculate Proportionality of the Highest Spatial Concentrations of Liquidity within Walking Distance to a Subway Station

Calculate Area Utilizing ‘Field Calculator’ & Export

Interactively Select Highest Ranked Polygons & Export

Combine All Network Distances Utilizing ‘Dissolve’ & Export

Phase 7

Manhattan Boundary with Calculated Area (Polygons) 5F

Divide Area within Walking Distance from Subway Stations by Total Area of Manhattan & Report

Repeat and Control for Desired Years

Divide Area of the Highest Spatial Concentrations of Liquidity within Walking Distance to a Subway Station by the Highest Spatial Concentrations of the Total Area of the Highest Liquidity & Report

Calculate Proportionality of Manhattan within Walking Distance to a Subway Station

Final Map & Determination (Polygons & Percentages)

7ABCDEF

02 concentrations Phase 1 Residential Real Estate Sales (Table)

Clean, Organize & Omit Commercial Real Estate, $0 Sales, Control for Years Geolocate by Address & Zip Code Utilizing Dual Range ‘Geocoder’ & Export

Subway Station Locations (Points)

Street Network (Lines)

Green Space (Polygons)

Water (Polygons)

Borough Boundaries (Polygons)

Verify Projection

Interactively Select Manhattan & Export Verify Projection

Residential Real Estate Sales, 2006

Phase 2

Phase 3

Locations of Residential Real Estate Sales within the Last Decade (Points)

Classify Kernel Densities by Standard Deviation Utilizing ‘Reclassify’ & Export

Select by Location within Manhattan Boundary & Export

Select by Location within Manhattan Boundary & Export Manhattan Boundary (Polygons)

Kernel Density Map by Transactions Ranked Decision Layer (Raster) 3A1

Kernel Density Map by Price / Unit Ranked Decision Layer (Raster) 3A2

Calculate Highest Ranking Spatial Concentrations of Liquidity by Conducting Map Algebra Utilizing ‘Raster Calculator’ & Export

Kernel Density by Transactions 4σ 3σ 2σ

Kernel Density Map by Unit Count / Sale Ranked Decision Layer (Raster) 3A3 3A

Manhattan Subway Station Locations (Points)

Manhattan Street Network (Lines)

Manhattan Green Space (Polygons)

Manhattan Water (Polygons)

Calculate Network Distance & Generate Buffer Ring by 0.25 mi Utilizing ‘Network Analysis’ & Export

Calculate Area Utilizing ‘Calculate Geometry’ Kernel Density by Unit Count / Sale 3σ 2σ 1σ

Exhibited here is the way in which we begin to understand the highest spatial concentrations of residential real estate market liquidity in Manhattan in 2006. First, residential real estate sales / transactions are geolocated to provide the basis of the spatial analysis. Second, the three variables of market liquidity in that regard are defined and a kernel density calculation is conducted for each with a search radius of 0.25mi to reflect walking distance. Third, the results are classified by standard deviation to appropriately gauge outliers in a statistically prudent way. The three variables taken into consideration are number of sales / transactions, number of dwelling units per transaction, and price per dwelling unit.

+ Kernel Density by Price / Unit 4σ 3σ 2σ

Phase 4 Weighted Decision Map of the Highest Spatial Concentrations of Liquidity (Raster)

Convert Raster to Polygon Feature Class Utilizing ‘Raster to Polygon’ Calculate Area Utilizing ‘Calculate Geometry’ Interactively Select Highest Ranked Polygons & Export

Walking Distance to Subway Stations (Polygons)

Calculate Area Utilizing ‘Calculate Geometry’

4BC

Base Map (Polygons)

4DE

Calculate Area Utilizing ‘Calculate Geometry’

Highest Spatial Concentration of Liquidity Σ Kernel Density by Transaction Count 4σ 3σ 2σ Kernel Density by Unit Count / Sale 3σ 2σ 1σ Kernel Density by Price / Unit 4σ 3σ 2σ

Phase 5 Weighted Decision Map of the Highest Spatial Concentrations of Liquidity with Calculated Area (Polygons)

Determine Highest Spatial Concentration of Liquidity within Walking Distance to a Subway Station Utilizing ‘Clip’ Calculate Area Utilizing ‘Field Calculator’ & Export

Map of Manhattan within Walking Distance Subway Stations with Calculated Area (Polygons)

5BC

Calculate Proportionality of Manhattan within Walking Distance to a Subway Station Divide Area within Walking Distance from Subway Stations by Total Area of Manhattan & Report

Manhattan Boundary with Calculated Area (Polygons) 5F

Exhibited here is the result for the weighted summation of the three aforementioned variables. Each variable is ranked by their distance from the mean of the kernel density calculation, where the highest ranking score is extracted and reported. Finally, we are able to gauge the highest spatial concentrations of residential real estate liquidity.

Highest Spatial Concentration of Liquidity Σ

03 distances Phase 1 Subway Station Locations (Points)

Street Network (Lines)

Green Space (Polygons)

Water (Polygons)

Borough Boundaries (Polygons)

Phase 2

Phase 3

Verify Projection

Select by Location within Manhattan Boundary & Export

Verify Projection

Select by Location within Manhattan Boundary & Export

Verify Projection

Select by Location within Manhattan Boundary & Export

Verify Projection

Select by Location within Manhattan Boundary & Export

Interactively Select Manhattan & Export Verify Projection

Manhattan Boundary (Polygons)

Manhattan Subway Station Locations (Points)

Manhattan Street Network (Lines)

Manhattan Green Space (Polygons)

Manhattan Water (Polygons)

Calculate Network Distance & Generate Buffer Ring by 0.25 mi Utilizing ‘Network Analysis’ & Export

Subway Stations

Phase 5

Phase 4 Walking Distance to Subway Stations (Polygons)

Combine All Network Distances Utilizing ‘Dissolve’ & Export Calculate Area Utilizing ‘Calculate Geometry’

5BC

4BC

Base Map (Polygons)

4DE

Map of Manhattan within Walking Distance to Subway Stations with Calculated Area (Polygons)

Calculate Area Utilizing ‘Calculate Geometry’

Calculate Proportionality of Manhattan within Walking Distance to a Subway Station Divide Area within Walking Distance from Subway Stations by Total Area of Manhattan & Report

55% of Manhattan was within walking distance to a subway station in 2006.

Manhattan Boundary with Calculated Area (Polygons) 5F

Exhibited here are the locations of every subway station in Manhattan in 2006 and the area that is within walking distance to them. Walking distance is defined by 0.25mi by street / network distance (not euclidean distance). First, the perscribed distance from all subway stations in Manhattan along the street line network are calculated. Second, a polygon for each is produced that adheres to that distance. Third, the summation of all polygons is produced and the area calculated. It is here that we can begin to report global calculations regarding walking distance to subway stations. Walking Distance to Subway Stations Subway Stations 0.25 Buffer from Subway Stations

04 proportionalities Phase 6 Map of the Highest Spatial Concentration of Liquidity within Walking Distance to Subway Stations with Calculated Area (Polygons)

Calculate Proportionality of the Highest Spatial Concentrations of Liquidity within Walking Distance to a Subway Station Divide Area of the Highest Spatial Concentrations of Liquidity within Walking Distance to a Subway Station by the Highest Spatial Concentrations of the Total Area of the Highest Liquidity & Report

Walking Distance to Subway Stations Subway Stations 0.25 Buffer from Subway Stations Highest Spatial Concentration of Liquidity Beyond Walking Distance to a Subway Station Within Walking Distance to a Subway Station

Phase 7 Final Map & Determination (Polygons & Percentages)

Repeat and Control for Desired Years

52% of the highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station in 2006.

7ABCDEF

Exhibited here are the final results of the analysis for 2006. By segregating the highest spatial concentrations of residential real estate liquidity by 0.25mi street / network distance from subway stations, a calcuation can be made between the two areas, which reveals the proportionality of market liquidity that fits beyond and within walking distance to subway stations. Here we see that the proportionality between the highest spatial concentrations of residential real estate liquidity is lower when compared to Manhattan globally.

Highest Spatial Concentration of Liquidity Beyond Walking Distance to a Subway Station Within Walking Distance to a Subway Station

05 time

52%

The same analysis can be repeated for every year from and including 2006 to 2016. of the highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station.

Exhibited here are the final results for every year from and including 2006 to 2016, utilizing the previous methodology for residential real estate transactions / sales in 2006. This shows the temporal changes and how we might better understand how phenomenon such as these change over time, knowing they are not static. In addition, conducting the analysis for multiple years allows for a small sample size to conduct brief classical statistical analysis and time series analysis to add depth to the finds in 2006

Highest Spatial Concentration of Liquidity Beyond Walking Distance to a Subway Station Within Walking Distance to a Subway Station

2006

68%

55%

of the highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station.

2009

2010

55%

62%

of the highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station.

2007

2008

60%

61%

of the highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station.

2011

2012

71%

61%

of the highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station.

2013

2014

48%

59%

of the highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station.

2015

2016

59% of the average annual highest spatial concentrations of residential real estate market liquidity were within walking distance to a subway station in the last decade.

55% of Manhattan was within walking distance to a subway station in the last decade.

Exhibited here is the spatial summation of all the previous results for every year from and including 2006 to 2016.

Highest Spatial Concentration of Liquidity Beyond Walking Distance to a Subway Station Within Walking Distance to a Subway Station

2006 - 2016

Exhibited here are the results from the previous analysis in both a statistically descriptive illustration, as well as a time series illustration. This affords the ability to analyze the central tendencies and temporal trends of the previous spatial findings. The histogram exhibits the distribution of the spatial findings and appropriately displays that the yearly average percentage of the highest spatial concentrations of residential real estate market liquidity within walking distance to a subway station was higher than observed globally in Manhattan for the mentioned years. The line graph exhibits how this relationship as it occures throughout time for the previously mentioned years, further emphasizing the temporal undulation of the subject while accounting for time as a factor in addition to space.

Percentage of Manhattan within Walking Distance to a Subway Station from 2006 to 2016

Frequency

n = 11 Îź = 59.27 Ď&#x192; = 6.66

Average Annual Percentage of the Highest Spatial Concentration of Residential Real Estate Market Liquidity within Walking Distance to a Subway Station from 2006 to 2016

Average Annual Percentage of Area within Walking Distance to a Subway Station

Average Annual Percentage of Area within Walking Distance to a Subway Station in Manhattan from 2006 to 2016

Highest Spatial Concentration of Residential Real Estate Market Liquidity Total Area of Manhattan

70 60 50 40

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

07 postulation

The average annual percentage of the highest spatial concentrations of residential real estate market liquidity within walking distance to a subway station was higher than the percentage observed globally in Manhattan within the last decade. What that means is, walking distance to a subway station may explain the highest spatial concentrations of residential real estate market liquidity in Manhattan. However, it is still unclear to what degree greater or less than is a substantive significant finding. Further exploration in theory and analysis is recommended in order to more appropriately provide additional insight. More robust spatial time series analysis than the one conducted here could explain much in the way of the subject.

08 appendix 1A

New York City Department of Finance. “Detailed Annual Sales Reports by Borough: 2006 - 2016 New York City Rolling Sales Data.” New York City Department of Finance. 2017. http://www1.nyc.gov/site/finance/taxes/property -annualized-sales-update.page

New York City Metropolitan Transportation Authority. “Subway Stations.” New York City Open Data. 2017. https://data.cityofnewyork.us/Transportation/Su bway-Stations/arq3-7z49/data

New York City Department of City Planning. "Lion." 17C. BYTES of the BIG APPLE. New York City Department of City Planning. 2017. https://www1.nyc.gov/site/planning/data-maps/ open-data/dwn-lion.page

New York City Department of Parks and Recreation. "Parks Properties." New York City Open Data. 2017. https://data.cityofnewyork.us/City-Government/ Parks-Properties/rjaj-zgq7

1E 1F

New York City Department of City Planning. "Borough Boundaries." New York City Open Data. 2017. https://data.cityofnewyork.us/City-Government/ Borough-Boundaries/tqmj-j8zm