• Does the underground affect the ground?

    Land Value and the NYC Subway System

There are many factors involved in the valuation of land, and infrastructure plays an important role in this process. For the purpose of this study, we have identified two types of infrastructure: hard infrastructure and soft infrastructure.

  • Hard infrastructure includes physical features such as roads, bridges, subways, tunnels, parks etc.
  • Soft infrastructure features security (e.g. police stations), cultural and recreational centers, schools, fire departments etc.

Economists have long argued for the existence of a causal link between public spending on infrastructure and land values. Using data collected by New York City’s Finance Department, the present study aims at examining this relationship.

Below are the current New York City subway lines that are analyzed for their relationship to land value:

– Lots next to Station – Lots 0.25 miles from Station | Click on station and hover over lines to view values

Click each column to sort by alphabetical order or value. A darker shade of red or blue signifies a higher value.

– Lots next to Station – Lots 0.25 miles from Station | Click on station and hover over lines to view values

Click each column to sort by alphabetical order or value. A darker shade of red or blue signifies a higher value.

– Lots next to Station – Lots 0.25 miles from Station | Click on station and hover over lines to view values

Click each column to sort by alphabetical order or value. A darker shade of red or blue signifies a higher value.

– Lots next to Station – Lots 0.25 miles from Station | Click on station and hover over lines to view values

Click each column to sort by alphabetical order or value. A darker shade of red or blue signifies a higher value.

– Lots next to Station – Lots 0.25 miles from Station | Click on station and hover over lines to view values

Click each column to sort by alphabetical order or value. A darker shade of red or blue signifies a higher value.

The reason for this  study is to provide data and evidence in support of the assumption that land values are heavily influenced by the presence or absence of good infrastructure nearby. Economists have long suspected this relationship. However, empirical studies to support it have been rather scarce.

One of the most known “landmarks” of New York City is its subway system. Since its creation in 1904, it provides 24-hour transportation services. It is a faster and more convenient means of transportation to get from point A to point B while avoiding surface traffic. This study posits that the existence of a subway system adds value to land in NYC and that land located near a subway station fetches a higher market value than land that is farther from the station, assuming that other infrastructure such as security (i.e. police, fire department) is also present.

The subway lines that are analyzed in this study are: 1, 7, G, N, and L. The average land value per square foot was found for every station on each line. These lines were selected because of the boroughs they serve and the distance they cover.

Property Valuation and Assessment Data Tax Classes 1,2,3,4: https://data.cityofnewyork.us/City-Government/Property-Valuation-and-Assessment-Data-Tax-Classes/8y4t-faws/data

Digital Tax Map – New York City Department of Finance: http://gis.nyc.gov/taxmap/map.htm

Text Maps for Subway Lines: https://new.mta.info/maps/subway-line-maps

Google Maps: https://maps.google.com

When calculating land value data for land near subway stations, we identified 8-10 lots around the subway station. We used Google Maps to find the location of each station, followed by the NYC Digital Tax Map to find the appropriate lot and block numbers. With the necessary lot and block numbers, we retrieved the land value of each lot using the PYMKTLAND (“Market assessed land value”) filter from 2020 found in the NYC valuation and assessment database. Once the data for all of the plots were found, an average was calculated, thus creating an average value per square foot of land.

When calculating land value data for land 0.25 miles away from subway stations, we identified 3-4 lots from each cardinal direction (north, south, east, west). The same filters and methodology were used as when calculating average land value for lots near subway stations. By using lots from all four cardinal directions, we ensure that no one direction weighs more heavily than another on the average value.

The land value data (that is based on 2020 data) provided by the NYC Department of Finance is compiled for valuation and taxation purposes. Thus, it is not the most recent data available. The PYMKTLND (“Market assessed land value”) filter that we used is NOT based on (previous) sale price.

The 0.25 mile radius was chosen because of the condensed nature of the NYC subway system.  If a larger distance were chosen, the plots of land would overlap with other subway stations. Another problem that would arise is running out of land (hitting water) when choosing plots of land that are farther than 0.25 miles from a chosen subway station.

This case study is designed to show general trends. We strive to be as accurate as possible, but limitations exist.

Via statistical analysis, we were able to find the following results for each line regarding whether subway stations had an influence on land value:

H0: This is the null hypothesis. It will act as our parameter through a hypothesized value that suggests that there is no statistical significance or relationships in a set of observations.

d: This is our alpha level. It shows us the statistical level of significance through a threshold value. Common alpha levels include 0.10, 0.05, 0.01, and 0.001.

p: This is our p-value, a conditional probability. It tells us how likely it is that the data would have occurred by random chance. The lower the p-value, the more statistically significant the data is. It is not the probability that the null hypothesis is true. If the p-value is lower than our alpha level, we reject the null hypothesis. If the p-value is higher than or equal to our alpha level, we fail to reject our null hypothesis.

1 Line – Since our alpha level d = 0.05 and our p-value is lower than alpha, we reject H0. This shows us convincing evidence that there is an increase in the average price of land close to 1 Line subway stations compared to the price of land that is 0.25 miles away from these subway stations. Therefore, there is evidence that supports the idea that land close to 1 Line subway stations is more valuable than land that is 0.25 miles away from these subway stations.

7 Line – Since our alpha level d = 0.05 and our p-value is lower than alpha, we reject H0. This shows us convincing evidence that there is an increase in the average price of land close to 7 Line subway stations compared to the price of land that is 0.25 miles away from these subway stations. Therefore, there is evidence that supports the idea that land close to 7 Line subway stations is more valuable than land that is 0.25 miles away from these subway stations.

G Line – Since our alpha level d = 0.10 and our p-value is higher than alpha, we fail to reject H0. This is not convincing evidence that there is an increase in the average price of land close to G Line subway stations compared to the price of land that is 0.25 miles away from these subway stations. Therefore, there is no evidence that supports the idea that land close to G Line subway stations is more valuable than land that is 0.25 miles away from these subway stations.

N Line – Since our alpha level d = 0.10 and our p-value is lower than alpha, we reject H0. This shows us convincing evidence that there is an increase in the average price of land close to N Line subway stations compared to the price of land that is 0.25 miles away from these subway stations. Therefore, there is evidence that supports the idea that land close to N Line subway stations is more valuable than land that is 0.25 miles away from these subway stations.

L Line – Since our alpha level d = 0.05 and our p-value is lower than alpha, we reject H0. This shows us convincing evidence that there is an increase in the average price of land 0.25 miles away from L Line subway stations compared to the price of land that is close to these subway stations. Therefore, there is evidence that supports the idea that land close to L Line subway stations is LESS valuable than land that is 0.25 miles away from these subway stations.

There are many factors that influence land value – e.g. location, interest rates, market optimism, demand, supply, level of preparation for buildings, land characteristics, accessibility, expected use of land, existing structures, teardown costs.

While in most cases land that is closer to subway stations is more valuable than land farther away, there were some cases where this was not true. This may be attributable to the absence of soft infrastructure such as security or poorly maintained hard infrastructure. By and large, the study seems to validate the Henry George theorem according to which, public investments in infrastructure tend to positively affect land values.

This project focuses on the New York City subway system but its implications could be wide-ranging. Further studies will be conducted in other major cities in order to strengthen or refute the hypothesis presented in this project.

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