A report on the impact of transit development on affordable housing

Author: Surya Jacob

Abstract

A larger proportion of the growing population in many North American cities consists of immigrants, millennials, young families, and working professionals. Cities are places that attract people while contributing to good experiences and facilities. However, as cities grow to accommodate the new incoming population, the services are extended to cater to people’s needs. This paper examines the relationship between rapid transit and the availability of affordable housing. The choice of transit and housing are interrelated. Accessibility to transit also leads to more job opportunities and other services. Hence, the study is focused on analyzing the degree of gentrification due to a new transit development in the neighborhood. Furthermore, the paper examines the need for affordable housing close to transit and analyzes the current residential development market next to rapid transit.

1. Introduction 

Whether in a developing nation or a developed country, the rapid growth of private automobiles  has led to congestion and accidents, but low-income and disabled individuals are excluded from  access. (Richardson, 1999). The promotion of public transportation is one of the sustainable  elements of transportation systems. (Sisi Yan, 2012). According to Kramer, affordability for people  who wish to live close to transit is essential. (Kramer, 2018). Requiring access for everyone in the  household, low-income groups may prefer reliable public transit over the woes of owning a private  vehicle and the associated expenses. Young adults and women may also prefer taking rapid  transit for various daily needs. It is therefore essential to clearly understand the current demand  while building housing around transit. A fair transport system for people with limited access to  private transportation is enhanced by public transport. (Sisi Yan, 2012) 

Researchers in the past have found a positive impact by new transit infrastructure on the “quality  of life, health, and activity patterns, greater accessibility and social inclusion,” especially for those  who do not own a private vehicle. (Foth, 2013). The paper aims to examine the extent of  gentrification fetched by new transit developments and the resident’s perception of its influence  in their neighborhoods in the city of Charlotte.  

The research reports two studies that determine the change in existing property values during the  planning, construction, and operational phases. The other is focused on understanding the  resident’s insight on the effect of a new rail system by comparing two neighborhoods with similar  characteristics. However, one lacks significant developments.

2. Literature Review 

The newest trend of Transit-oriented development is to link high-density housing, jobs, and high quality transit to increase accessibility, shorten commute and encourage more travelers to ride  transit, walk, and bike. Moreover, if this theory is carried out in practice, the resultant experience  of travel will reduce the vehicle miles traveled and, therefore, greenhouse gas emissions. (Alex  Karner, 2014). 

Baker argues the fact that these foreseeable benefits may give rise to gentrification. Property  values and rents in the areas close to the stations are more likely to increase as they become  more desirable due to the transit factor. Consequently, the group of low – and – moderate-income  residents and minorities with the most need for and who can benefit from the value-added systems  may not reside in the areas. Baker also emphasizes that developers often target higher-income  residents around LRT station areas by building condominiums and upscale housing. The studies  on light rail and transit-oriented developments primarily use land and property value changes as  gentrification indicators. (Dwayne Marshall Baker, 2017) 

For instance, while studying the effects of light rail planning on vacant residential property in  Portland, Oregon, Baker, according to a study by Knaap, Ding, and Hopkins, 2001 found “that  plans for light rail investments positively impact land values in proposed station areas.” According  to their study, the results indicate that planning for new development such as a light rail system  causes housing prices to rise and potentially prices out the low-income residents. (Dwayne  Marshall Baker, 2017). According to the 2000 Census Data, the higher-income households who  use transit less are close to those developments in Atlanta, Baltimore, Chicago, Cleveland, Dallas,  Denver, Los Angeles, Portland, San Diego, San Francisco, St Louis, and Washington D.C. The  study in 2010 claims that low and moderate-income individuals who needed this development  were displaced further away. It is also evidence that no housing policies exist to protect against  the increasing land values, home values, and rents. (Cozart, 2017). 

(Dawkins, 2016) found that home prices are 6 percent to 45 percent higher near transit stations  than other parts of the city through a literature review by Cervero, 2004. Additionally, a recent  analysis established that houses close to transit demand 4 percent higher rents than other  properties. The condition is viewed in areas within a 0.25 miles extent of public transit. (Casey  Dawkins, 2016). The woes faced by the low-income groups due to gentrification are that they  often tend to choose an alternative that costs them more in terms of longer commutes and  compromises access to other essential services. 

3. The impact of Light Rail Transit 

3.1 A study on change in property values in the light rail neighborhood in Charlotte, NC 

To answer how rail transit causes gentrification and the provision of affordable housing, the LYNX Blue Line of Charlotte City is considered an example to explore this effect. 

The study begins by looking at reports on Mecklenburg County’s light rail system in Charlotte,  North Carolina. The city’s light rail system has a few unique characteristics, such as it is a short line located in a low-density urban region, and it uses the track of a former freight line. (Sisi Yan,  2012). 

The Blue line of the LYNX system opened on  November 14, 2007, is 15.5 km long and connects the Charlotte CBD to the suburban area to the south of the city. Among the 15 stations, five of them are located in the CBD. Park-and-ride locations are available for seven southernmost stations farthest from the CBD. One of the studies that support the research is focused on one type of housing, single-family homes, where the structure and land are owned by one entity. Due to insufficient samples of single-family houses, the area in grey as depicted in Fig. 2 is not included in the study.  

Charlotte, in recent years, has become the  20^th most populated city in the nation. According to the Mecklenburg County Planning Commission, the growth rate is about 3.45 percent when calculated between 1980 and 2005 (Sisi Yan, 2012). The Charlotte Area Transit System (CATS) opened its first light rail line, the LYNX Blue Line, which runs from the city center to the southwest in 2007. As a result of this new and house price capitalizations were found along this line. Yan (2012) adopts a longitudinal approach and studies a period of 11 years from 1997 to 2008 from planning the light rail system to its operation. The study is characterized in four phases which include the time before there were any concrete plans for a light rail system (t1 =  1997-1998), the planning phase for the light rail (t2= 1999 to 2005), its construction (t3= 2005 to  2007) and its operational time (t4= 2007 to 2008).  

The average impact across all of the light rail stations is assessed. Yan (2012), uses sales price data from Property Ownership Land Records Information Systems owned by Mecklenburg  County. Additionally, the records also store data on the size of the house, the year it was built,  the number of bedrooms, bathrooms, fireplaces, and the quality of the building. A few filters were applied to omit the extreme values in the houses that are not consistent with the market value.  Also, the study considered only houses within a 1-mile distance from the stations. A set of explanatory and control variables like the age of the house, the heated area, were also used to model price fluctuations. The critical variable in the analysis is the distance from the station. (Sisi Yan,  2012).  

Yan uses a hedonic regression model at the block group level to understand the distribution of housing prices and the light rail station. The variables used in the study include proximity to rail stations, housing characteristics, and neighborhood fixed effects, which represent each area under the study. The analysis is repeated for each of the four phases.  

According to the study, before the rail system began operation, there was a negative influence on home prices, most likely due to industrial land use around existing stations. At t1, t2, and t3, the positive value of the correlation coefficient of housing price and the proximity to the light rail indicate that houses tend to have higher values when located at a greater distance from the station. It is expected that larger houses may experience a higher sales value. Nevertheless,  housing prices started to react positively to the light rail investments during its operational phase. 

The probable elucidations are that access to reliable transportation has improved the attractiveness of single-family houses in the new transit-oriented neighborhood and the area’s improved face after the industrial usage was terminated. Several multifamily houses and commercial properties have started to distillate in the area. (Sisi Yan, 2012). 

From Table 1 and the regression studies from Table 2, the distance coefficient depicts that proximity to light rail stations contributes modestly to variation in housing values. The coefficient at all times is positive but at a decreasing rate. It is significantly smaller at t₄ than the other periods and matches the assumption that proximity to a light rail station contributes positively to house prices compared to other periods. The regression result also reports that the distance coefficient is not different in the planning (t₂) and construction phase (t₃) of the light rail system from the preplanning stage (t₁). 

The initial effect, as reported, narrates that Charlotte has significantly less congested traffic than Boston or Chicago, which reduced the pull of living close to light rail, which also results in limited awareness of public transportation. It appears that the rail investment did not affect Charlotte’s single-family house prices until it became functional. (Sisi Yan, 2012) 

3.2 The changes after the extension of LYNX

Another study to understand the effects of light rail in the City of Charlotte is discussing the report by Nilsson. In 2018, an addition to the Blue line became operational and extended north from the center city through neighborhoods such as Belmont, Villa Heights, and NoDa, to single-family minority neighborhoods and finally terminating at the UNC Charlotte main campus. Nilsson performed a study around this new line and its stations through in-person surveys. He was keen on answering the following research questions: Were the residents in Charlotte’s new rail station neighborhoods more likely to consider moving from their neighborhood, and what was the resident’s perception of the new station’s effect on their neighborhood? (Isabelle Nilsson, 2020) 

In this study, the neighborhoods along the light rail line are considered as the treatment neighborhood compared with another nearby neighborhood that has not experienced any significant investments. The comparison neighborhood has minimal differences with the treatment neighborhoods and was identified using characteristics such as population density, race average housing age, type of housing, and average home sales price.  According to the initial observations for the study, it is found that the treatment and comparison neighborhoods have a larger share of Blacks and Hispanics and a lower share of Whites as compared to the entire light rail extension corridors. It also had lower incomes, home sales prices, 

and rents. The neighborhoods also had lower shares of single-family housing compared to the entire light rail study area. The houses in the treatment and comparison neighborhood are cheaper than the rest of the county, and the homeownership rates were lower. (Isabelle Nilsson,  2020).  

According to Fig 4, Nilsson asserts that the entire light rail corridor experienced a sharper increase in home values in 2000 and 2013 compared to the treatment and comparison groups. Rents further increased at a faster rate compared to the county and leveled off between 2013 and 2018  up to the opening of the light rail. The treatment and comparison groups showed similar trends,  particularly in terms of rents and in home values. However, it is found to be at a slower rate compared to the county during both time periods. (2009-2013 and 2014-2018). Therefore,  out-migration of lower-income households is not expected to be seen compared to the other neighborhoods in the city.  

A survey conducted between August 2018 and October 2019 was in lower-income and minority neighborhoods along the extension line. Due to a lack of trust in research methods, the data was collected in person at neighborhood events, light rail stations, parks, and walkable areas in the treatment neighborhood. On the other hand, in the comparison neighborhood, surveys were conducted at an apartment complex, a library, a YMCA, and shopping centers. A sample of 289  from the treatment neighborhood and 115 from the comparison were used after eliminating incomplete survey responses.  

The study is conducted within the first year of starting operations and the results are positive..  One-third of the residents in the treatment neighborhood and around 50 percent in the comparison neighborhood are considering moving shortly. This result was further explained emphasizing that the share of Black respondents in the entire light rail area is much higher than expected.  Additionally, there was a larger share of Blacks in the treatment neighborhood while more white and Hispanic residents were in the comparison neighborhood. The results say that those who have lived in the neighborhood for a more extended period are less likely to move as they are familiar with the area, the people and have created connections. (Isabelle Nilsson, 2020) .  

The respondents in the comparison group, as seen in Fig 5, have reported “Other” and “Rent/ Value Change” as reasons for moving from the neighborhood. Some of the common factors included in “Others” comprise downsizing in families, transfer of jobs, college, or graduation. 

As noted, it appears that light rail was not a strong determinant in considering possible that the rent/property value change could be due to the light rail.  However, from Fig 5, the most common reason for living in a neighborhood is that housing costs in the area and home sales prices are relatively low in this move. In the treatment neighborhood,  it is county compared to elsewhere as per the survey respondents. In both neighborhoods, renters are more likely to consider a move in the coming years. In effect, studying the reason for moving from a neighborhood, the responses are very similar between the treatment and comparison group, and the difference between two stated reasons, “Rent/Value change” and “Other,” are insignificant.

The study’s variable of interest aims to find whether distance to the nearest station matters when a resident’s tendency to move. Fig. 7 from Nilsson’s study reveals that the distribution between those considering a move vs. staying appears to be similar and signifies yet again that the proximity to the station does not influence the probability of moving. (Isabelle Nilsson, 2020). A  prominent reason Nilsson suspects for lower mobility in the treatment neighborhood is that it has a strong sense of community and higher tenure rates.  

Out of 590 survey respondents from all of the light rail neighborhoods, Nilsson explains that 147  participants wrote a response to this question, and around 39 mentioned the light rail along with other factors as a reason to move. Among the 192 respondents of the comparison neighborhood,  50 participants reported overall city population growth, increased demand for housing, and limited availability of affordable housing as reasons to consider a move. Some of the responses included housing upgrades or improvements, gentrification, new homes or businesses in the area.  

Approximately 64.5 percent were firmly optimistic about the light rail’s effect on their neighborhood. Only 17.5 percent had adverse reactions about this addition to the city, for reasons such as gentrification, increased rent and property tax, displacement, noise, increasing crime by bringing people from other areas, and not enough bus connectivity. Since the reason cited most for moving from an area is housing/rental prices, we could conclude that the introduction of the light rail has affected this, even though that might not be the only reason. It has been found that some of the areas in the city have had rising housing values, but since the Light rail’s effect was noted as the top reason (39 out of 147 respondents) it can be given credit for some of those rising values, both in rent and housing as per Nilsson’s investigation. 

3.3 Recent News Reports  

Both the studies reported were based on analyzing the change to existing property values after the functioning of LRT. However, recent news reports claim the absence of affordability in new developments close to the LRT. It states that when the LRT was planned, a policy was adopted by the City Council to provide 5 percent of affordable units and a maximum of 25 percent units below the market rate in any multifamily housing development. This policy was made to ensure that people do not spend more than 30 percent of their income on housing and utilities, which also permits them to save on transportation. The investment resulted in a scenario where the 

policy did not attract affordable housing but, on the other hand, attracted all the luxury apartments with amenities such as fitness centers, pet spas, and pools, and the rents were found to be over  $2000 per month. (Kelly, 2019) 

Reports have seen that since 2007, the South end embarked on new developments and turned into a happening hub for young professionals who preferred to live near the tracks. A similar scenario is seen in the areas of light rail extensions from uptown to UNC Charlotte. The city has not set aside even a small portion for lower-income groups. The city’s housing and neighborhood  development director said, “A successful rail line includes diverse price points, and it is not just  for the wealthy people.” (Fred Clasen-Kelly, 2018) 

Lately, around 6200 units have been built along the line, and precisely 100 are below-market units. One of the reasons the city could not meet its’ affordable housing policy, as given in the  Brooklyn to Ballentine Report, 2019, emphasizes that it did not buy land along the proposed route and was unable to donate or sell land at a discounted rate to make affordable housing units. There were also citizens and officials mainly from the affluent neighborhood of Ballentine that were against the planning director’s suggestion to promote affordable housing along the rail line. One of the comments made was as follows, “we do not want it in our districts regardless of what it  looks like or whom it serves.” These oppositions can put the city in a tight spot to provide equitable services to all. (Kelly, 2019) 

The articles also mention that affordable housing was not given high priority when the line was planned. The continuous growth, high land prices, and out-of-town investors also make the provision of affordable housing more difficult.  

Conclusion 

From the studies conducted in Charlotte, it can be concluded that the new transit development can have a significant impact on new real estate developments in the area. The first analysis is based on only existing property values, and the study is conducted soon after the LRT started functioning. However, this cannot be generalized as the area of the study is limited to one city and further studies need to be conducted elsewhere to see if this statement can be generalized. 

Furthermore, it will provide additional records for reinforcing the idea that transit developments can cause gentrification. Another possibility could be studying if the property values differ from station to station depending on the amenities and services next to it. 

The second study is more extensive and focused on gentrification and people’s perception of new development in their neighborhood. Since the study is based mainly on survey responses which could be biased, focusing on studying the state’s policy for planning transit and developments around it and analyzing people who live in the area from time to time may contribute to robust evidence. However, the study proves that light rail or proximity to the station was not a strong determinant in considering a move in both the neighborhoods and some of the other reasons such as rent or property value change, transfer of jobs, new businesses were dominant. 

The provision of affordable housing should tightly relate to housing policies. It is essential to understand the extent of gentrification and displacement in other cities with rapid transit to establish and further investigate the topic. The paper is focused on the impact of the light rail system in Charlotte with the intention to keep in mind the similar effects of gentrification in other cities. Although many factors contribute to affordability, reducing transportation costs can help reduce one main expense of each household.

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