Bay Area Walk Score Premiums - Unlocking Value Through
Neighborhood Trends
by MASSACHUSETTS INSTITUTE OF T.CMLOGY Nicholas ForanSEP
13
707
B.S., Architectural Engineering, 2009The University of Texas
LIBRARIES
ARCHIVES
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate in Partial Fulfillment of the Requirements for the Degree of Master of Science in Real Estate Development.
at the
Massachusetts Institute of Technology September, 2017
@2017 Nicholas J. Foran All rights reserved
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created.
Signature redacted
Signature of Author_
Center for Real Estate July 28, 2017
Signature redacted
Certified by
Professor Albert Saiz
Daniel Rose Associate Professor of Urban Economics and Real Estate, Department of Urban Studies and Center for Real Estate
/
Accepted by
Signature redacted
ssor Albert Saiz
Daniel Rose Associate Professor of Urban Economics and Real Estate, Department of Urban Studies and Center for Real Estate
Bay Area Walk Score Premiums - Unlocking Value Through
Neighborhood Trends
by Nicholas Foran
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate on July 28,
2017 in Partial Fulfillment of the Requirements for the Degree of Master of Science in Real Estate Development.
ABSTRACT
The digital age of real estate provides access to new data and techniques to evaluate properties. Real estate brokerage and technology firms are assembling this data to produce user-friendly scores that serve as powerful metrics to identify real estate trends and evaluate buyer behavior. This paper examines Redfin's "Walk score" that measures a location's walkability to amenities like grocery stores or parks and uses a hedonic pricing model to find the $/square-foot premium for high Walk scores in three communities in the San Francisco Bay Area. The data is composed of residential transactions from 2014 to early 2016 that are analyzed at the neighborhood level and normalized to improve the precision of the hedonic model. This neighborhood lens produces a more robust analysis than the broader data sets used in the majority of prior Walk score research. The results shown in this paper demonstrate that a high Walk score is highly correlated with increased property values in a broad range of communities with diverse socioeconomic characteristics. This study includes a framework for using Walk scores (and several related scores) by discussing the composition of the scores, economic principles underpinning them and the critical assumptions for hedonic regressions using Walk scores. These considerations are critical to assessing the real premium of Walk scores. The paper concludes with an analysis method for investors to use walk scores to identify real estate home-buying trends, find under-valued property and create development programs that leverage and build upon walkability.
Thesis Supervisor: Professor Albert Saiz
Acknowledgements
I want to thank the many people who contributed to this thesis and whose support brought me across the finish line. I would especially like to thank Professor Saiz, Dr. Schery Bokhari, and Libby Seifel. Their knowledge, experience, and care shaped this paper and made the journey of writing it fun and full of great learning experiences. I am so grateful to each of them for their generous guidance.
Further, the support of my classmates and the Center for Real Estate Staff and Faculty have made this year an amazing experience which I will never forget. Thank you and I am excited to continue my career alongside such brilliant people.
Finally, I would like to acknowledge God and my family. Their consistent love and
Table of Contents
1 Introduction ... 5
1.1 Digital Real Estate Data and the Rise of Property Scoring ... 5
1.2 The Grow ing W alkability Trend and the W alk Score M etric... 6
1.3 W alk score's History and Com ponents ... 6
1.4 Live, W ork, Play and the Econom ic Principle Behind the Consum er City... 8
1.5 The Value of W al k Score ... 9
2 Literature Review ... 10
2.1 Current State of Research ... 10
2.2 Public Transit's Interaction w ith W alkability ... 16
2.3 Walk Scores Are a Consistent Measure of the Pedestrian Environment... 17
2.4 Planner's Intuition and Visual Analysis ... 18
2.5 Lim itations of Current Research ... 18
3 M ethodology...19
3.1 Data Source and Grouping ... 19
3.2 Study Targets the M ajority of the Population and Elim inates Outliers ... 25
3.3 Hedonic Regression Set Up ... 26
3.4 Assum ptions w ith W alk score and the Geography Dem ographics... 27
3.5 Om itted Variable Bias, Sim ultaniety Bias, and Data Accuracy... 27
4 Results and Discussion ... 28
4.1 Results...28
4.2 Incorporating Walk Score (and Other Scores) into Your Real Estate Strategy. ... 37
5 Conclusion...38
1 Introduction
1.1 Digital Real Estate Data and the Rise of Property Scoring
There has never been more data available for real estate investors to identify value-drivers and new trends. The data network is so immense and far reaching it can be overwhelming. It covers
everything from the number of people who commute by bicycles to testing scores for high school students in a given area. Many real estate leaders emphasize using data to inform investment decisions but this can be a tumultuous endeavor for the faint hearted analyst. As a result, real estate technology firms and brokerages are packaging the data into scores.
The data used to create a score is often aggregated across multiple sources. Advanced GIS systems, google maps, and even consumer-generated information are used to create one, united score. These scores use a consistent algorithm that allows an observer to compare a score in New York City to one in ranchlands of El Paso. The scores provide a benchmark built around a cohesive system of analysis.
Redfin, a leading web-based real estate brokerage, is a major proponent of real estate scoring. They have developed a set of scores dealing with mobility as well as the labor market that are very valuable to real estate consumers. The scores are given for every address and can range from 0 to 100. The most popular scores are:
* Walk Score: Access to personal needs (across 7 different categories) within a 30-minute walk. * Bike Score: Bike lanes, hills, connectivity, and percentage of commuters biking to work. * Transit Score: Proximity and "usefulness" of public transit
* Opportunity Score: Jobs within a 30-minute commute on public transit that pay more the $40,000 a year.
Walk and Transit score will be discussed later with a high level of detail. Opportunity score is very useful but not as influential in car commuting communities. It was not used as a variable in this study because of its correlation to Transit score. Bike score is probably the most unique because its calculation uses data about how consumers choose to go to work.
With such availability of data, some scores are published that lack rigor and analysis
fundamentals. This leads to accuracy issues and misleading results. This is an important considerationof this study and for consumers. The Redfin scores were selected for this study because they have a clear methodology and sources. The exact calculation of a Redfin score is proprietary but each score is validated by researchers at leading universities (23*, 24*, 25*). This paper will also lay out assumptions that should be carefully considered when reviewing scores. Sufficient perspective is critical to utilizing a score appropriately. Further, analysis methods that transform the score using natural log or other functional forms are risky because they completely changing its meaning or undermining the score's components.
Scores when used correctly can be great tools to gauge consumer preference. Many scores are newly developed and have the potential of indicating new trends. Property value drivers may be captured by a new score methodology and expedite a property's due diligence process. Similarly,
consumers are often unaware of the scores when making real estate decisions and the score can act as a hidden indicator of consumer preferences. Savvy investors benefit from real estate scoring and
1.2 The Growing Walkability Trend and the Walk Score Metric
One of today's most predominant real estate trends is urbanization or people's relocation back to dense, urban areas. People across generations are choosing to live and work in areas where they can walk to restaurants, grocery stores, or even their hair salon. Walk score is a metric that captures this walkability. There are a broad set of meanings for walkability and this paper focuses on it as a location's access to a variety of amenities. The definition of amenity has been broadened in recent years.
Amenities go beyond a pool or luxurious apartment lounge. They are the very context of a location. What can the consumer access on a daily basis? How can their lives be shaped by the activity and diversity of resources that are around them? This is a crucial aspect of walkability. Walkability can also address street designs and planning decisions but this paper exclusively examines it as a locations' access by foot to basic needs including grocery stores, coffee shops, and other categories that will be discussed later.
Walk score has become an increasingly important tool for consumers. People can use it for selecting an apartment, buying a house, or deciding their next office location. The highest level of interest in walk score is on the West Coast. These communities actively look to technology solutions when making decisions. Consumers increase their efficiency by looking to Walk score for the amenity density of their next home instead of trying to calculate the distance to each amenity themselves. Municipalities on the West Coast are actively using walk score in the planning and permitting process as well as in the public policy realm. Walk score technology establishes benchmarks for urban leaders to make data-driven decisions. Walk score has reached as far as Australia and continues to grow in its influence. A recent survey conducted by Redfin demonstrates this popularity and the report states:
"Fewer than 2 percent of active listings are considered a walker's paradise (Walk Score of 90 and above). Yet 56 percent of millennials and 46 percent of boomers prefer walkable communities with a range of housing amidst local businesses and public services"
1.3 Walk score's History and Components
Walk score was introduced in 2006 and since 2011 every location in the U.S.A. has a rating. Redfin acquired Walk score in 2014 and began to aggregate the Walk score data with the rest of its MLS data. Redfin describes the score as follows:
"For each address, Walk Score analyzes hundreds of walking routes to nearby amenities. Points are awarded based on the distance to amenities in each category... A decay function is used to give points to more distant amenities, with no points given after a 30-minute walk. Walk Score also measures
pedestrian friendliness by analyzing population density and road metrics such as block length and intersection density."
The data sources for Walk score include Google, Education.com, Open Street Map, the U.S. Census, Localeze, and places added by the Walk Score user community. Walk scores consist of
components, each with their own score, which all contribute to the final score. If the amenity is located within a quarter mile (5-minute walk) of the address, it is given a score of 100. Redfin recently made public the API (application program interface) showing the score of each component for a given property (Figure 1). Interestingly, commercial office proximity/density is not a category. Walk score
Figure 1: Example of Scoring Components of Walk score
100%
50%
Source: Redfin
The categories cannot be simply averaged because Walk score does have a small component of
block length and density. This makes Walk score's algorithm challenging to untangle. Nonetheless, the
major components of Walk score are the amenity category scores. Another item to note is that the algorithm for walk scores is independent of transit score. Transit scores exclusively show a location's access to mass transit. Similarly, bike scores do not contribute to walk scores. Walk score and transit
score are often used interchangeably but their fundamentals are very different. Together they indicate the overall mobility of a place and are both useful tools depending on the consumer.
The final Walk score is described further by Redfin to give consumers an idea of the walkable character of a location (Table 1). Independent of one's work commute, these descriptions lay out the degree of car dependency for a location's everyday needs and wants. This interaction of Walk score and car dependency is integral to the value of Walk score. This will be discussed around fundamental
economic principles later in this paper. Table 1: Descriptions of Walk score Ranges
Walk score Description
90-100 Walker's Paradise
Daily errands do not require a car.
70-89 Very Walkable
Most errands can be accomplished on foot.
50-69 Somewhat Walkable
25-49 Car-Dependent
Most errands require a car.
0-24 Car-Dependent
Almost all errands require a car.
Source: Redfin
1.4 Live, Work, Play and the Economic Principle Behind the Consumer City
Consumers are more and more shaping large real estate developments. Successful developer's must respond to consumer's preferences. Redfin currently finds that many leading metropolises (Philadelphia, Boston, Seattle, Chicago, San Francisco) see the majority of new development in areas with a Walk score higher than 73. Mixed-use developments have seen a resurgence alongside the growing urbanization trend. Consumers go to mixed-use locations not only to access a variety of amenities, but also to do so in a quick and enjoyable fashion. This ethos is at the core of the live, work,
play mentality. Theories around this inform much of the urban planning and design paradigms of the 2 1st century.
Many researchers have employed economic principles to better align good urban design with market forces. The consumer centered approach is based on the economic principle of utility. This is the satisfaction or usefulness a consumer finds in using a good or scarce resource. Price is the mechanism by which markets efficiently allocate these goods. In other words, people are will to pay based on how
useful a good is to them. Kelvin Lancaster further developed this principle in a theory that states goods
have multiple characteristics which summed together give rise to the utility. Nase confirms that this can be adapted to property markets because the consumer derives his/her utility through the package of characteristics for a property. These characteristics include square footage, finishes, year built, location
and even surrounding amenities.
Walk score is a critical part of applying the economic principle of utility to real estate because it
communicates the accessibility and diversity of the surrounding amenities. Consumers are willing to pay more to increase their utility from these amenities. If a young professional can increase the speed of
picking up groceries or dry-cleaning, this translates into more hours for work and a likely increase in their bonus. This is the most concrete example but it applies to all individuals and definitions of value that are non-monetary. Today's great urban developments like Hudson Yards or Brickell City Centre
recognize the significant influence of the utility principal and center their design around it.
Location is a scarce resource and not easily replaced. Therefore, the utility of a location and
surrounding amenities are also scarce making them integral to a locations value. Nase states that
developers implicitly lean on this economic principle when trying to command higher rents through more useful locations and surroundings. Rosen developed a method of valuing these characteristics by
hedonic regression which is fundamental to our study. The characteristic of a location's usefulness for everyday needs is a key variable in the regression. This study uses Walk score to best capture this aspect
Consumers consistently search for utility at the lowest cost. However, this looks different as societies evolve and cultural norms change. For instance, compare these two demographics from different eras. A young millennial couple with no children who work in tech and the same young couple from the 1960's with several children who have a single income from manufacturing. Utility looks very
different between these two families. The young millennial couple may value personal space less than the couple with a family. The millennials may value easy access to their work, other tech professionals,
night life and car independence. Conversely, the young couple from the 1960's may value privacy, a garage for the latest car, and a playground in their backyard for the kids.
There are many new lines of research that attempt to understand how today's consumers are using the city. A novel example of this is a study that uses Google street view to capture changes in the number of pedestrians in an area. Another is tabulating the amount of transparent glass at the street level to assess pedestrian engagement at the street level. Ewing found this type of building transparency is more successful at generating traffic having tree-lined streets. Saiz focused on the impacts of leisure amenities in a city. These "Beautiful Cities" attracted a disproportionate number of highly educated citizens and increase housing values in supply inelastic markets. Other types of research in spatial econometrics attempt to place a numerical value on the spatial components of utility. Tobler formed the foundation for these spatial effects when he found "everything is related to everything else but near things are more related than distant things" (First Law of Geography). This study stops short of full
econometric spatial analysis but relies on some of this theory and limitations which will be discussed later.
These types of research are influencing developers and cities to move to performance based metrics. Planners and developers are moving away from their own training and powers of perception to a more quantitative analysis that directly responds to the actions of the urban consumer. These
performance metrics are tests of the economic principle of utility. They examine a development's influence on people's productivity and generating traffic. An anecdote or observation of a successful development is helpful, but not as much as actual performance results. Walk score is a useful
benchmark when comparing performance and can be used to evaluate the aspects of utility consumers want. Economists applaud this market responsive process and its roots in the principle of utility.
1.5 The Value of Walk Score
Value can have many meanings and a variety of perspectives. This paper focuses on economic value, specifically residential home transactions, because it is quantifiable and widely understood. Additionally, this papers aims to create actionable results for real estate investors and developers. These groups are most interested in walk score's correlation to property transaction premiums. As with all trends, the current premium is important as well as the growth of this premium over time. This study
primarily focuses on the former due to limited data availability. However, there are strong indications that the most profitable aspect of the walkability trend is how it changes over time. Gilderbloom,
Leinberger, and Duncan find the increasing consumer preference for walkable neighborhoods is driving up this premium. The added value of walk score is spreading from trendy metropolis' such as San Francisco to the towns of East Bay such as Walnut Creek. A trend that was intangible and unquantifiable prior to Walk score can now be quickly assessed and compared.
Developers see the value of walkscore and are using it to expand the realm of amenities for a project. As mentioned, amenities now cover walkable locations around the project including parks,
grocery stores, and nightlife. These add immense value to the property and increase its marketability. Google maps recently opened an interface that can be used to calculate areas that fall within a 10-minute walk of a location (Figure 2). Some developers place this on their brochures to sell the walkable amenities of a project.
Figure 2: Oakland 10 Minute Walk Map.
Source: Mapnificeint, Oakland, CA.
The core of Walk score's value as a metric is that it quickly communicates the information in this map in
a numerical format. The highlighted area shows the resources within a 10-minute walk radius of the location. Walk score calculates this as well as an extended walking radiuses (up to 30-minutes) to calculate the amenity density and diversity. A walk score provides someone that's new to the area a simple and quick evaluation of the amenities in the area.
2 Literature Review
Real Estate in conjunction with Redfin. Bokhari approached walkability through data-driven lens and his research is a hedonic regression study of residential transaction premiums at the MSA level
(Metropolitan Statistical Area). The research quantified the value of 1 point of Walk score (Table 2) and the value of increasing a Walk score from 60 to 80 (Figure 3). There is a large spread between cities with regard to the value of high Walk scores. Homes in Phoenix only receive a $16,000 increase in value while the homes in San Francisco receive a $188,000 boost.
Table 2: Value of 1 Point of Walk Score on Median Home Value
$ Premium of 1 % Premium of 1
Walk Score Median Sale Walk Score
Metro Area AeaePieWalk Score Point on PononMda
Average Price MdaHoePie Point on Median
Median Home Price Hm rc
Home Price Atlanta 48.4 $168,000 $2,838 1.69% Baltimore 68.7 $229,900 $652 0.28% Boston 80.7 $325,000 $3,927 1.21% Chicago 77.5 $220,000 $2,437 1.11% Denver 59.9 $285,000 $2,410 0.85% Los Angeles 66.3 $475,000 $3,948 0.83% Oakland 71.6 $523,000 $1,735 0.33% Orange County 43.5 $580,000 $114 0.02% Phoenix 40.3 $204,900 $217 0.11% Portland 63.9 $275,000 $1,210 0.44% San Diego 49.9 $449,000 $2,205 0.49% San Francisco 85.7 $950,000 $3,943 0.42% Seattle 72.9 $375,000 $3,603 0.96% Washington DC 77 $360,000 $4,386 1.22%
Figure 3: Median Price Home Premiums for Increasing Walk Scores from 60 to 80 By city, in thousands Phoenix $16 Baltimore $33 Orange County $41 Portland $53 San Diego $68 Chicago $78 Atlanta 84 Denver 84 Oakland $86 National $106 Seattle $116 Boston $129 Los Angeles $129 Washington D.C. $133 San Francisco $188
Source: MIT Center for Real Estate and Redfin
Bokhari arranged his analysis to account for the non-linearity of the walk score premium by using a natural log fit for the dependent variable ($ transaction premium). Figure 5 shows the critical Walk score changes that produce significant increases in a property's value. These jumps demonstrate the linearity between increases in Walk score and increases in transaction premium. This non-linearity sense when one thinks about the categories mentioned in Table 1. Certain increases in walk
score begin to eliminate the need for a car and therefore add significant value. The low end of the spectrum (moving from a Walk score of 19 to 20) adds little value ($188) while the upper end (moving from a Walk score of 79 to 80) adds a large amount of value ($7,031). This coincides with the utility
principle mentioned earlier. A person may no longer need a car and their utility will drastically increase when Walk scores increase from 79 to 80. Increases on the lower end (19 to 20) still leave the consumer
Figure 4: Critical Value Jumps for Increases in Walk Score $7,031 $7, 000 $5,000 E S4,000 = $3.744 $3,000 S2,000 51,704 S1,000 S181 19-20 39-40 59-60 79-M0
Source: MIT Center for Real Estate and Redfin
Cortright also analyzed walkability at the city level. He groups all the residential transaction data together and accounts for similar variables to this study with the exception of distance to CBD. Most
cities (12 out of the 14) showed more walkable areas did have higher home values. Bakersfield came out statistically insignificant and Las Vegas resulted in a negative relationship between high Walk scores and home values.
Boyle examined the premium for walkability using a fixed effect regression instead of standard
OLS. This results show no statistical significance for Walk score; however, Boyle acknowledges the fixed
effects in the regression also impact the Walk score variable. The fixed effects were meant to account for the neighborhood dynamics (quality of schools, crime, etc) but end up also including aspects of Walk
score such as proximity of parks and schools. The theory behind fixed effects is they remove unobserved heterogeneity which is a common problem for cross-sectional data sets. These data sets often violate the regression assumption that the residual has a constant variance. This assumption is critical to finding
unbiased coefficients and accurate standard errors. This means that the variation (residual) of an independent variable is not consistent across the range of predictions for the dependent variable. This condition is known as heteroscedasticity which can lead to biased coefficients and standard errors that are too small. Boyle's work informed the regression set up in this study. This model approaches fixed effects from a different angle but maintains Boyle's goal of finding results that are reflect the
neighborhood dynamics.
Boyle gives other reasons for the loss of statistical significance of Walk score. The foremost is that the neighborhood He considered for fixed effects has a limited variation in Walk scores. This means that most of a neighborhood region is either walkable or un-walkable. A one-sided neighborhood is then
built in to the fixed effect (regression constant) leading to inappropriately-weighted results. Boyle also states that the reason Walk scores in the past have shown a premium is because the areas are more developed and have more amenities. Our study does not confirm or deny this statement and runs parallel to it. Our model is focused on determining if Walk score itself is a good metric for analyzing the value of a property.
There are several major differences in this study when compared to Boyles. First, Boyle used a random sampling of 3500 transactions with a unique address. This means that if more than one unit transacted at the same address then only one random unit was considered. Second, Boyle's sample is small with regard to the market of focus (Miami) and had a very broad demographic. Unlike this study, Boyle did not narrow the data to see the preference of a focused demographic and this drastically impacts the result (mansions, farmland, and micro units are included in Boyle's study).
Boyle's findings provide insight to the influence of the neighborhood on Walk score premiums. Our study accounts for these neighborhood effects by adding a neighborhood box. This helps account for the influence of crime and schools on the regression result. Boyle's main point is that other studies must acknowledge the importance of the neighborhood in contributing to home value. He concludes saying "we believe that future research may show that average neighborhood walkability has a strong and direct relationship with the value of the neighborhood-specific fixed effect" and walkability will likely be more valuable as density increases and car use decreases. This regression study agrees and upholds this ethos by accounting for neighborhood variation.
Leinberger from the George Washington University School of Business has published a wide array of papers focusing on the nature of walkability in different metros. His research makes observation about the most walkable areas at smaller geographic areas such as downtown. Leinberger uses data on a basic, averages analysis to show growing premiums and growing market share (Figure 5). This is not an advanced valuation or analysis model but gives the general theme of walkability's upside. He also develops his own categories which include walkup, walkable neighborhood, drive-able edge city and drive-able suburban (most to least walkable). The break down is based on walk score as well as other building size/use parameters. These categories contribute to his analysis by clearly showing the difference in value and growth (Figure 6).
Figure 5: Share of Income Property in Metropolitan Area that is Walkable 60% 50%
U
1992-2000U
2001-2008 2 2009-2014 40% 1--30% 20% 10% 0BOSTON WASHINGTON, ATLANTA MICHIGAN
D.C. METROS
COMBINED Source: George Washington University School of Business, 2015
Figure 6: Boston Average Home Sale Price per Square Foot
Average Home Sale Price:
(Cost per Square Foot)
$450 $400 $350 $300 $250 $200 $150 $100 $50 s 2004 2006 2008 2009 2010 2011 2012
Source: George Washington University School of Business, 2015
WALKLUP WALKABLE NEIGHBORHOOD DRIVABLE EDGE CrTY DRIVABLE SUB-DIVISION 2013 -.. ..
.-Walkability has influenced other sectors of real estate beyond residential. Pivo found the value premiums for walkable commercial real estate (office, retail, apartments) to range from 6% to 54%. He also found lower cap rates suggesting that investors view walkable properties as lower risk. Doherty's results demonstrate a national trend of premiums (higher rents and reduced cap rates) for walkable properties. Beyond Real Estate, Gilderbloom as well as Doyle focused on walk score's correlations to crime, health, and even upward mobility. Each of these studies found noticeable trends with regard to
high Walk scores and positive effects on the community.
2.2 Public Transit's Interaction with Walkability
Many researchers have focused on what aspect of mobility is the most important value driver. Duncan in his study of San Diego housing prices finds that properties close to transit with average or even poor pedestrian environments have no significant sale premium. At the same time, properties with good pedestrian environments have a premium of up to 15%. A good pedestrian environment is
classified as a place with high intersection density, at least 4 jobs per hectare, and minimal slope. The leading driver amongst the variables is job density.
Figure 7: Pedestrian Environment's Influence on Condo Sales Near Transit.
$180,000 -$170,000.Ix.
I
$160,000 $150,000- $140,000- $130,000- $120,000- $110,000- $100,000-0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 Kiometres to Nearest Trolley Sttim1.2 1.3 1.4 1.5 1.6
$90,0r~I
...
-iiiijl~ll --.---
-Source: Urban Studies, January 2011.
Transit is often seen as the most important mobility parameter. Duncan's research questions about this assumption. The premium between public transit and walkable neighborhoods is challenging to untangle but studies like Duncan's are a great start. When one considers the most vibrant and highly demanded areas of the city, seldom does a key transit stop come to mind. Transit is important but consumer's instinct attracts them to street activity and access to useful goods/services. This is part of what makes Soho in New York, Chinatown in San Francisco, Back Bay in Boston and South of Congress in Austin so memorable. When transit and walkability come together, it creates highly sought after places. Transbay terminal in San Francisco or the Oculus in New York are good examples of major transit hubs providing a variety of amenities to create a very walkable experience. The interaction between transit and a walkable environment and how they complement each other is a key consideration for
developers.
2.3 Walk Scores Are a Consistent Measure of the Pedestrian Environment.
Walk score is a web-based geospatial algorithm and does not take into account any current socioeconomic or demographic information. The algorithm requires no "feet on the ground" and solely assembles different pieces of data from a GIS road and zoning network. Previous research shows these composite measures are more predictive of actual resident walking than any single parameter (Carr). Carr in another study with researchers at the Harvard School of Public Health examined many walkability metrics such as intersection density as well as amenities (see Figure 9 for all parameters).
The results showed Walk score is most precise when considering a 1-mile radius from the
address. This is in alignment with Walk score's claim of evaluating a location's context up to a 30-minute
walk. Interestingly, most all of the parameters were statistically significant. However, as soon as the radius drops to / or % mile the correlation between Walk score and key walkability metrics diminishes slightly.
Table 3: Correlation between key GIS walkability parameters and Walk score
GIS Walkability Parameter X mile radius / mile radius 1 mile radius
rs p-value r, p-value r, p-value
Retail destinations (density) 0.53 <0.0001 0.67 <0.0001 0.8 <0.0001
Service destinations (density) 0.27 <0.0001 0.53 <0.0001 0.67 <0.0001
Cultural/educational destinations 0.44 <0.0001 0.53 <0.0001 0.69 <0.0001
(density)
Parks (density) 0.24 <0.0001 0.37 <0.0001 0.51 <0.0001
Median pedestrian route directness 0.24 <0.0001 -0.01 0.7908 -0.05 0.2166
Intersection density 0.51 <0.0001 0.59 <0.0001 0.65 <0.0001
Cul de sacs (count) 0.01 0.7024 0.14 0.0002 0.37 <0.0001
Source: Carr, Harvard School of Public Health
Retail was the dominant parameter for all radius regressions (correlation of 0.80 at a mile radius). Cul-de-sacs and Highway density showed the lowest levels of significance at around 0.40. This confirms the
theme that Walk score is predominantly influenced by the surrounding amenities. Many other
characteristics that are objective and calculable. Research as described above confirms Walk score is an adequate measure of a location's walkability.
2.4 Planner's Intuition and Visual Analysis
Finally, there is significant research related to the value of walkability that is more anecdotal in nature. SPUR, a proponent of walkability, started to review the characteristics of certain locations that made walkability popular. SPUR states:
"But building densely and mixing land uses such as housing, jobs and retail do not in themselves deliver the many benefits of urbanism. The different uses must be integrated into 'complete neighborhoods,' places that are designed for people and serve their daily needs comfortably and efficiently within close walking distance... 'Walkability' is excellent shorthand for good urban design"
ULI (Urban Land Institute) is a think-tank and professional organization for dense, urban areas. They
have observed:
"The 'Place-making Dividend' [is] the intrinsic value that accrues to a community when districts possess a strong sense of place that in turn results in high levels of repeat visits, increasing rents, retail sales, leasing demand, and capital value. Such a dividend occurs when individual real estate projects are so well designed and interconnected that they work as one integrated place."
Walk score assists this goal by measuring the "completeness" and "integrated" amenities of a
place. Metrics like Walk score communicate and validate these industry best practices of good urban
design.
2.5 Limitations of Current Research
The limitations of the research follow two patterns. First, very little data-driven research exists
that zooms in on the neighborhood level. The trends identified in research are at a macro level and
although they consider neighborhood effects, the trends are not actionable. Investments are made at a
neighborhood or even block level and this is where research is needed in order to inform real estate development and investment. The primary reason for this is that data is limited at this zoomed-in lens. This study aims to improve this gap in the research by analyzing scores at the neighborhood level. Some of the studies use simple averages to find the difference in value between very walkable urban areas
and suburban. The problem with this approach is the urban areas are more expensive for a whole host
of other reasons. This is where hedonic regressions can provide more insight. There are several regression studies in Walk score premium that consider the fixed effects of neighborhood but this research does not target the premium for a specific neighborhood or part of town. More hedonic regression research is needed at the neighborhood level.
Second, there is no research that uses regressions to consider how walkability and amenity access preferences are changing over time. The most important aspect of walkability to the real estate
investor is if it is becoming more valuable or if the trend is spreading across the country. Again, the lack of data limits the ability of researchers to draw such conclusions. The urbanization trend may be largely driven by consumers changing appetite toward amenity rich environments, but research has yet to
3 Methodology
3.1 Data Source and Grouping
This analysis will focus on residential transactions for specific neighborhoods in the East Bay. The data set is from Redfin and distributed to Dr. Bokhari at the Center for Real Estate who allowed this study access. It consists of a digital MLS database combined with the Redfin scores and other location
parameters. The residential sector is a leading indicator of consumer preference and is well suited for understanding the value of walkability. This data is a compilation of all residential real estate
transactions from January 2014 to January 2016 for the Bay Area.
The data set was refined by insights gained from Dr. Bokhari's research and specifically Figure 5 mentioned above. The premium for low walk scores is minimal and largely washed out by other
variations in value. High Walk score are in high demand and command a significant premium. The data sets were categorized to focus on the premium for different ranges of walk score. The ranges are set so as to gain sufficient variation between the categories. It also resembles Redfin's groups based around
car dependency. The breakdown helps distinguish the value of walk score outside and above other value
drivers. Similarly, Transit scores have been broken down into categories to quantify their value. The
ranges for Transit scores are different than those for Walk scores. This again was toward the goal of
testing a sample with sufficient variation between the categories.
Figure 8: Walk Score Categories for Analysis.
0
to 65
V
Low: Pratically
no walkability.
65 to 85
Medium: Many
daily needs and
wants walkable.
*
85 to 100
High: Almost all
daily needs and
wants walkable.
Figure 9: Transit Score Categories for Analysis.
0 to 50
V
Low: Little to no
transit access.
50 to 70
Medium:
Limited access
to transit..
70 to 100
High: Transit readily accessible.The analysis will focus on moderate to dense neighborhoods from a range of demographics. Due to the expensive nature of Bay Area housing prices, the income demographics are skewed to the middle and upper class. As seen in Figure 10, the distribution of price per square foot follows a normal
distribution. The median price for the Bay Area is very high compared to the rest of the country. The distribution pattern and high median price point is true for each neighborhood studied. This is a limitation of this research and would benefit from application in other more affordable cities.
Figure 10: Price Per Square Foot Distribution of Overall Sample
$0 $1000
Price per square foot (USD)
The data set does not include affordable housing and all transactions are at the market rate.
This was key to maintaining the integrity of the Walk score premium results and ensuring they reflect market rates. The sample has a range of home ages, conditions and configurations. Townhomes and condos along with single family are include and the mix. Each of the areas selected have access to BART
which is the predominant means of transportation in the area. Most also have access to AC Transit or
Caltrain which are also widely used transit systems. The selected neighborhoods of focus are far from
homogeneous and cover a variety of demographics and socioeconomic backgrounds. Table 5 shows the overall statistics for the cities involved in this study.
The areas were set by their geographic coordinates and are small enough to capture the general character of a geography. The neighborhood boxes can be viewed in Figures 11, 12, and 13. The study took care to review school and crime maps and adjust the boxes accordingly to avoid major outliers. For example, Walnut creek was separated along the 24 highway because of the different school quality and
crime rate north and south of that latitude. In an ideal world, the bounds would contain even smaller
areas but this would create a problem of limited transactions and therefore statistical unreliability. The
key to a statistically relevant data set is that there is sufficient distribution of transactions between categories. If there are too many in the low Walk score category and not enough in the high one, then
Table 4: School, crime, economic and score statistics for the cities of interest Annual Crime Rate per 1000 Residents 75 40 22 Number of reported incidents for 2016 (property and violent crime) Population 420,005 69,122 233136 (Estimate) 2016 U.S. Census Density (Persons per square mile) 7,004 3,248 2764 2010
U.S.
Census Median Income $54,618 $82,120 $105,355 (USD) 2015 U.S. Census Walk Score Average 72 39 44Redfin City Report
Bike Score Average
61
N/A
61
Redfin City Report
Transit Score Average
55
N/A
36
Redfin City Report
Opportunity Score Average 64 36 35 Redfin score calculation (jobs within 30 minutes on public transit) Oakland Walnut Creek Freemont Public School Ranking 695th 95th 138th 2015-16 Statewide Test Scores
,
Figure 11: Geography of Coordinate Areas for Analysis Oakland
hEml
4& WE$9OALAVU afston ampbd mpaack London Squarr.
W TOWCrAS. r
I
1=-i
a4
OUTHFl 1- f f~ Sibley. VolcanIc Regional 4 ---st A 'Ban 64i . Oe - lR 4N>~ PerCRSOaan efj t / c 'i n' '~~13
I Park Fd NATOW I a -- I ma SAN A '4 NTONI:cQ
M ilk r^1l rRUITVALE NETO AcdAve,L
ac.Coostm \f EnstMWChabot Space &
Science Center
Joaquin Redwood Miller Park Regional Park
0n f l Regior 47-8 ~ Va__ Et4T 0imwey S BE 'Sp&ce HI is
Figure 12: Geography of Coordinate Areas for Analysis Walnut Creek I a '-4 %esAve A 4 -t A. S -I
--1
4 b 4: 1-i: NLEY 04I
t* 1on 4?N
,~,j$ *-,g ~ "sa 5. - 'M Oak F 7 1 Geary Rd San Lu"Rd Is -in 2r Sn Acalanes Ridge Open space. s4 SARANWA':1
LAFAYETTE. VALLEY ESTATE o RQOSSMOQR' rkA d Tyvale Av Contra Costa Centre A v Walnut Cr ek U U 0 .s5 Bancroft The HeaG4th Pt OV C Vt44\r
ens at erfarm. 4. PA it aVj7~
4 L4j~'xv
a ~ it nc-.3'. 'Fs2
WILD OAK. 0 s9OrAve ' e r RudgeaRd. U Sugarloaf Open Space s RUDGEi tSTATI ley B I -~ ... ---,-.r--- - ~ .~-49
4 a 0,Rossmoor-Walnt Creek 0 S VA L ST. 91 PFigure 13: Geography of Coordinate Areas for Analysis Fremont )ENWO9 6, 94 4% RIP 4 Ar. 1%4;5ot4i 'All A l RegIgn-I 5 (9,
r
'I
Newa* NewPark Mal W ^c&, It Q 11 F Frlront/
* tICT centrl(
aste Management --Cits dfl& SAYLANDS A2h
Olne Coleg BlvdJ
I
I --NNW"4
s
Ico!
The boxes are the best attempt to contain a neighborhood. School districts, crime and commercial/retail districts were often fluid. This model attempted to confine these influences in the coordinate areas but could not perfectly combine these factors. Nonetheless, the coordinate areas are of small enough and similar character to model a blend of the smaller neighborhood effects.
The latitude and longitude bounds turned out to better capture the variations between areas than an analysis by zip code. For instance, the zip code 94607 is a land area of 5.9 square miles which
covers parts of West and Downtown Oakland. There is a great deal of variation in density, character and crime rates in this zip code. There is almost no overlap between zip codes, school quality, and crime rates. As a result, our study chose to use the geographic coordinate boxes instead of controlling for zip code. The geographic coordinate boxes make Downtown Oakland one district instead of separating it into two zip codes. This is different than other studies were zip code is the conduit when controlling for
the fixed effects of a neighborhood.
Figure 10: West Oakland Zip Code (94607) Land Area Map
TEWSCAL.
Source: Gogye Map
MOCNT(
SM
Pkedmont
Th. std .ok .o .rnd w. re.r t. .h m.or ... theh pSuttn P.de nttkei
EMARCADM1 OU"TCT
~
0
AT&T Park
UNESource: Google Maps
3.2 Study Targets the Majority of the Population and Eliminates Outliers
This study looks for trends with regard to the majority of the population. It does not take into
account certain demographics including country/farm homes, high-end luxury estates, extreme micro-units, and mobile homes. The follow assumptions were built to eliminate these outliers:
0 Lot square feet < 500
* Lot square feet > 1 acre
* Finished square feet < 400 (eliminate micro unit properties)
* Finished square feet > 5000 (eliminate mansions)
* New construction (Structures less than 1 year old). * Year built < 1900
* Overall sale price > $3,000,000 (eliminate extreme luxury properties) * No employment in the adjacent areas
* Number of bedrooms > 10 or equal to 0 * Number of bathrooms > 10 or equal to 0
These refinements in the data set help focus on the demographics of interest and avoid extraneous result on demographics that make up a small part of the population. Overall roughly 10% of the transactions fell into the above categories for the cities of interest.
3.3 Hedonic Regression Set Up
Hedonic regression analysis, specifically OLS regression, is the most viable way to quantify the value of a real estate property component or variable. The regressions were calculated using the Stata Software. The dependent variable is the natural log of the price per square foot. This was used in lieu of overall transaction price because price per square foot (finished) is the predominant metric for
comparing property value. An important item to note is that the effects of escalation are a significant contributor to the regression and taken into account via the sale quarter variable.
The value-driving variables (independent) selected for this regression study follow the
precedence of similar studies. All independent variables (listed here) are fixed, independent and assumed to be without error::
* Walk Score Categories
" Transit Score Categories
" Square Feet of Finished Space
* Median Household Income
* Employment * Number of Beds * Number of Baths 0 Age * AgeA2 * Property Type * Sale Quarter
* Geographic Coordinate Area
The overall hedonic equation reads as follows:
Ln(Price per Square Foot) = a + b'*(Walk Score Catergories) + c'*(Transit Score Categories) +
d*Ln(Square Feet of Finished Space) + e*Ln(Median Household Income) + f*(Employment) + g*(Number
of Beds) + h*(Number of Baths) + i*(Age) + j*(AgeA2) + k'*(Single Family Premium) + l'*(Sale Quarter) + m'*(Coordinate Neighborhood Area)
The regression was ran using the "robust" Stata code. This essentially conducts the White test for heteroscedasticity. If heteroscedasticity is present in the regression, Stata adjusts the standard
errors to ensure they are robust (homoscedastic residuals). Robust standard errors have p-values that accurately reflect the statistical significance. If this code was not used, there is a chance of bias in the
assumes there are no fixed effects influencing the model. Fixed effects can help account for parameters like quality of the schools and other neighborhood effects. However, we accounted for these with additional neighborhood variables instead of overall fixed effects.
The t-statistic and standard error were used to evaluate the variable's statistical significance. Coefficients were considered very statistically significant if their t-statistic is higher than +/- 2.00 and standard error less than half the coefficient's value. The coefficients for each variable give an idea of the
influence on the overall price per square foot. The R-squared variable is not weighted as heavily because it indicates the overall fit of the regression line for all the variables. This study is not concerned about
being able to project the exact overall price for a property transaction. It is concerned about the influence of Walk score on this overall price. The trend of higher property values for high walk scores can be observed without having a R-squared term above 0.8.
The dependent variable is the natural log of the price per square foot. Natural log was the best fit for this distribution which follows the results from previous research in real estate p was used as is standard with Hedonic regressions. The natural log form embodies the character of diminishing returns for each variable as the increase to un-normally high numbers. For instance, once the number for
bedrooms reaches above 4 the value of each additional bedroom becomes less and less.
3.4 Assumptions with Walk score and the Geography Demographics
The assumptions for this regression study are divided in two ways. Walk score, an aggregation of multiple pieces of information, has assumptions associated with the metric. The score gives equal
weight to all amenities within a given category. For example, a Whole Foods is viewed equally to a
small-format grocer under the grocery designation. A large park like Central Park in New York would receive the same number of points as Bryant Park. The two have vastly different abilities, appeals and offerings
but are viewed as equal. Proximity is all that matters.
Walk score does not consider topographic or accessibility factors. Walk score is blind to sidewalk length, width, slope, ramps for handicap, protection from traffic, population density and security. All of these influence the walker's experience and the desirability of a location.
Last of all, the timing of the sale and newly opened amenities also influence Walk score. Score values for this study were tabulated at the time of sale; however, Walk scores are not updated
continuously. This means if a new amenity opens it may take some time for the score to update. Redfin
does not specify exactly how often a Walk score is updated.
3.5 Omitted Variable Bias, Simultaniety Bias, and Data Accuracy
The analysis selected independent variables that drive value with regard to the target metric
(price per finished square foot). The variables used capture the primary value driving components from previous studies and several more. Each variable was selected and examined with regard to the
neighborhood of interest and the market conditions. This thorough approach helps eliminate omitted variable bias. Variables like zip code, number of stories, and county were dropped for lack of statistical significance. Conversely, Variables like median income are a big overall value driver and included in the
model. The crime rate and school rating are two variables not included in the study due to the
challenges of modeling such fluid variables. These are large influencer of residential property value and
bias is suspect, these two may be contributors. Generally, the neighborhood box analysis eliminates many externalities like crime and school quality.
Our analysis is sensitive to overlap between the independent variables. This is called
simultaneity bias and can lead to false coefficients or under/over-valuing a particular variable. The main vulnerability for our study is the transit related scores. Often a high Walk score will be present in areas with high Transit scores. To address this issue, our analysis included both Walk and Transit Scores as value drivers and placed them in categories. Although there is correlation, the study used a high number of observations within each category and sufficient variety between walk and transit scores at a given address. Neighborhoods like Walnut Creek and Oakland were selected for this reason.
Data errors were limited by acquiring certified data from an MLS database partner. No data entry was completed by hand in the databases. The data was also scrubbed for outliers or errors by auditing the ranges and minimums/maximums for each variable. Finally, the data selection for the model was narrowed by eliminating limited interest properties such as mansions and farmland. Data quality issues may exist to a minimal extent but are unlikely to influence the regression results due to a sample size larger than 1,000 transactions for each model.
4 Results and Discussion
4.1 Results
The first model is for the overall Bay Area and gives an idea of the regional premiums for transit
and walkability. This covers the entire peninsula plus East Bay (as far as Concord and Pleasanton) and
Southern Marin County. The regression follows the constraints of the "areg" Stata code and allows for control of an additional independent variable. In this case, the model controlled for the local
municipality. This acts similarly to the neighborhood boxes although contain larger areas. The full
regression results for all the models can be found in the Appendix. Table 5: Overall Bay Area Model Coefficients and Errors
Overall Bay Area
Low Walk scores Baseline
Medium Walk scores -0.0315 0.0046 -6.79 -0.0406 -0.0224
High Walk scores 0.0724 0.0098 7.38 0.0532 0.0916
Low Transit scores Baseline
Medium Transit scores -0.0504 0.0098 -5.15 -0.0695 -0.0312
Figure 11: Median Price for Each Score Category (Overall Bay Area) High $376$401 0 OD .4-, $355 Medium $361 U $361 0 U
Low
$373
$300 $350 $400Median Price per Square Foot (all other variables equal) Transit Score Categories a Walk Score Categories
The overall model resembles the Walk score premium seen in other studies and shows statistically significant premiums for high Walk scores (> 85). The results are displayed in Figure 9 for a
median price per square foot for each category. The bar graph was projected using Stata's margins
function which holds all other variable equal and fluctuates the factor variable of interest (score
categories). This process produces statistically significant (high t-statistic) projections of the market rates for each score category. The graph shows the significant premium across the Bay Area for high Walk scores. Another key finding is that medium scores in both categories show a negative coefficient and a penalty to the price per square foot. This may be the result of these areas being under-developed or in transition. The fully developed areas with many amenities (high Walk scores) are seeing a significant premium. Areas in transition (medium Walk scores) appear to be less desirable to consumers.
Interestingly, the model finds a minimal premium for high Transit scores. This is likely due to the wide scope of this model covering the entire Bay Area. Many low-development areas like those in Marin
county or the southern peninsula are not walkable but still highly sought. The demand drivers of privacy and terrane make low Walk scores more valuable as well. Consumers can choose to live up a steep hill with a great view instead of living in more accessible areas with high Walk scores. Next we will discuss
Table 6: Oakland Model Coefficients and Errors
Oakland South Independent Variables
Low Walk scores
Medium Walk scores High Walk scores
Low Transit scores Medium Transit scores High Transit scores
Oakland 1 Oakland 2 Oakland 3 Oakland 4 Coef. Baseline -0.0825 -0.1185 Baseline -0.0864 -0.0988 Baseline -0.3806 -0.4790 -0.7404
Std. Err. (robust) t-staf [95
0.0236 -3.49 0.0356 -3.33 0.0318 -2.71 0.0472 -2.09 0.0509 0.0506 0.0534 -7.48 -9.46 -13.86 -0.1288 -0.0361 -0.1883 -0.0486 -0.1488 -0.0239 -0.1914 -0.0063 -0.4804 -0.5782 -0.8452 -0.2808 -0.3797 -0.6356 Oakland North lndependent V
Low Walk scores Medium Walk scores High Walk scores
Low Transit scores Medium Transit scores High Transit scores
Oakland 1 Oakland 2 Oakland 3 Oakland 4 Baseline 0.1230 Baseline 0.1738 0.0821 Baseline -0.2164 0.0828 0.0603 0.0302 4.08 0.0555 3.13 0.0506 1.62 0.0760 0.0720 0.0739 0.0637 0.1822 0.0648 -0.0174 -0.3656 -0.0585 -0.0848 -2.85 1.15 0.82 0.2828 0.1815 -0.0671 0.2241 0.2054
Figure 12: Median Price for Each Score Category (Oakland Models) $412 $365 $252 0 0 0 U a_ C 0
85 and above (High)
0 Oakland North U Oakland South
The regression models for the two groups in Oakland show very different results. The Oakland
model that covers downtown and the Northern neighborhoods sees a clear premium for high Walk as well as Transit scores. Consumers are willing to pay $47 (PSF) more for high Walk scores (<85) as
compared to medium Walk scores (65<85). The North Oakland model's baseline is for medium Walk
scores due to insufficient transactions with low Walk scores. Currently North Oakland is seeing a major resurgence from downtown San Francisco dwellers. They are custom to a very walkable life and are moving to areas around the peninsula that offer this same landscape which is driving the premium for high Walk scores. North Oakland is becoming safer, gentrified and surrounded by better schools due to this movement.
In comparison, the South Oakland model has a negative coefficient for medium to high Walk and Transit scores. This may be a result of the area being farther from the Downtown San Francisco and containing less gentrified neighborhoods. There is also the perception that South Oakland is less safe. The maps below layout the crime and school rating for Oakland. There is not a significant difference in
level of safety between the North and South neighborhoods. Yet, North Oakland is receiving a premium for high Walk scores while South Oakland does not. It is also important to note that North Oakland still has a high rate of crime when compared to surrounding areas
$500 $400 $300 $200 $100 65 to 85 (Medium) Score Category Cu 0) 0 to 65 (Low)
Figure 13: Internal comparison of Oakland crime (left) and school quality (right)
Bn erkeley nBerkeley
anyon Morga rv Canyo Moraga
Piedm Las Trg Piedmo Las-Tra
OR1 J, ; gio Wilder iegi Ame A ArnN Island sLendd, Source: Neighborhoodscout.corn
North Oakland is an example of an area where consumers are demanding high Walk scores. The premium is pushed up by the shortage of supply in walkable real estate. Investors can use the results of
this model to gain an idea of the significance of the Walk score premium in an area of high demand. Conversely, South Oakland is not seeing this demand for high Walk scores. This observation is also
important for investors as well as considering why this is happening. Will the walkability trend continue to reach further into Oakland? Are there safe and walkable areas of South Oakland that may appeal to
the Consumers of North Oakland? There may be real development opportunities with currently
under-valued real estate in South Oakland (discussed more in 4.2). Next we will discuss the very different neighborhoods of Walnut Creek.
n
Table 7: Walnut Creek Model Coefficients and Errors
Walnut Creek North
Independent Variables
Low Walk scores
Medium Walk scores High Walk scores
Transit scores
Walnut Creek 1 Walnut Creek 2
Walnut Creek South Independent Variables Low Walk scores Medium Walk scores High Walk scores
Transit scores Walnut Creek 1 Walnut Creek 2 Coef. Baseline -0.0289 0.2426 Omitted Baseline -0.3557 Coef. Baseline -0.2415 -0.2318 Omitted Baseline -0.5188
Std. Err. (robust) t-stat [95% Confidence
0.0510 -0.57 0.0840 2.89 0.0355 -10.01 Interval] -0.1290 0.0712 0.0778 0.4075 -0.4254 -0.2859 A4"bust) -0.3103 -0.6074 0.0351 -6.88 0.1916 -1.21 0.0345 -15.02 -0.1727 0.1439 -0.5865 -0.4511
Figure 14: Median Price for Each Score Category (Walnut Creek Models)
$560
$427
65 to 85 (Medium)
Score Category
85 and above (High)
N Walnut Creek North n Walnut Creek South
The North model in Walnut Creek shows significant premiums for high Walk scores. It covers downtown Walnut creek which is a denser, mixed-use area where most of the high Walk score properties are located. Walnut creek is a mostly suburban community and typically would not be
$439 $600 $500 $400 $300 $200 $100 -C 0 0 0 U. C, 0) C Ln a> M) cv 0 to 65 (Low)
thought of as a neighborhood desiring walkable homes. The premium of $121 per square foot for high Walk score real estate runs counter to typical suburban development attitudes. The South model has a negative coefficient for high Walk scores but a low t-statistic (-1.21). This is largely due to the limited number of high Walk score transactions and makes the results questionable. Looking at the medium Walk score category, the coefficient is negative and has a high t-statistic. This is statistically significant and shows that walkability is not a predominant value driver for South Walnut Creek. The Southern parts of Walnut Creek have better schools and this may lead to stronger demand for family-oriented, spread-out homes. Underlying high Walk scores is the concept of density. Very rarely will a less dense area be able to provide the mix of amenities to achieve a high Walk score. Density is a key building block for walkable real estate.
Figure 15: Internal comparison of Walnut Creek crime (left) and school quality (right)
~
Ple~asanIIv Four Comers
Boydd~ Rd H.
Lafayete
Allmo 05W
Source: Neighborhoodscout.com
It's hard to determine if Walnut creek consumers want to live in walkable areas. How broad is
the demand for walkable real estate as witnessed in the North Walnut Creek model? The low number of properties available in the high Walk score category may be due to zoning complexities which limit the
overall supply of walkable real estate. Citizens may also push-out walkable developments. Walkable housing may clearly be the most profitable and beneficial for a community but against the sentiment of
the voting public.
Another consideration is the car dependency of the East Bay region. Many of these towns and feeder cities require a car for daily needs. Cars bring more parking. Walkability and plentiful parking don't usually fit together well. This is where public transits interaction with walkability is key. Walk score, although not directly related to transit, does correlate well with good transit. Good transit enables households to be independent of a car which can increase the density of an area. In the Walnut
Creek area, the transit stations have large parking lots to allow commuters access. It is very common for people to drive to the transit stop and park instead of walking there. This may be why there is a negative
