Capturing the Value of Walkability
Abstract
1. Introduction
2. Literature Review
2.1. Value Capture
2.2. Walkability
3. Data
4. Methodology
5. Results
5.1. Global Model
5.2. Local Models
5.3. Exploratory Spatial Data Analyses
6. Discussion
7. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Wise, D. Public Transportation: Federal Role in Value Capture Strategies for Transit Is Limited, but Additional Guidance Could Help Clarify Policies. 2010. Available online: https://www.gao.gov/products/gao-10-781 (accessed on 28 October 2024).
- Levinson, D.; Istrate, E. Access for Value: Financing through Land Value Capture. 2011. Available online: https://www.brookings.edu/research/access-for-value-financing-transportation-through-land-value-capture/ (accessed on 28 October 2024).
- Vadali, S. Using the Economic Value Created by Transportation to Fund Transportation: A Synthesis of Highway Practice. 2014. Available online: https://nap.nationalacademies.org/read/22382/chapter/1 (accessed on 28 October 2024).
- Knight, R.; Trygg, L. Evidence of land use impacts of rapid transit systems. Transportation 1977, 6, 231–247. [Google Scholar] [CrossRef]
- Higgins, C.; Kanaroglou, P. Forty years of modelling rapid transit’s land value uplift in North America: Moving beyond the tip of the iceberg. Transp. Rev. 2016, 36, 610–634. [Google Scholar] [CrossRef]
- Debrezion, G.; Pels, E.; Rietveld, P. The impact of railway stations on residential and commercial property values: A meta-analysis. J. Real Estate Financ. Econ. 2007, 35, 161–180. [Google Scholar] [CrossRef]
- Mohammad, S.; Graham, D.; Melo, P.; Anderson, R. A meta–analysis of the impact of rail projects on land and property values. Transp. Res. A-Pol. Pract. 2013, 50, 158–170. [Google Scholar] [CrossRef]
- Hamidi, S.; Kittrell, K.; Ewing, R. Value of transit as reflected in U.S. single-family home premiums: A meta-analysis. Transp. Res. Rec. 2016, 2543, 108–115. [Google Scholar] [CrossRef]
- Yen, B.; Mulley, C.; Shearer, H. Different stories from different approaches in evaluating property value uplift: Evidence from the Gold Coast light rail system in Australia. Transp. Res. Rec. 2019, 2673, 11–23. [Google Scholar] [CrossRef]
- Herrmann, T.; Boisjoly, G.; Ross, N.; El-Geneidy, A. The missing middle: Filling the gap between walkability and observed walking. Transp. Res. Rec. 2017, 2661, 103–110. [Google Scholar] [CrossRef]
- Tuydes-Yaman, H.; Karatas, P. Evaluation of Walkability and Pedestrian Level of Service. In Engineering Tools and Solutions for Sustainable Transportation Planning; Knoflacher, H., Ocalir-Akunal, E., Eds.; IGI Global: Hershey, PA, USA, 2017; pp. 30–57. [Google Scholar]
- Litman, T. Economic value of walkability. Transp. Res. Rec. 2003, 1828, 3–11. [Google Scholar] [CrossRef]
- Brigham, E. The determinants of residential land values. Land Econ. 1965, 41, 325–334. [Google Scholar] [CrossRef]
- Ball, M. Recent empirical work on the determinants of relative house prices. Urban Stud. 1973, 10, 213–233. [Google Scholar] [CrossRef]
- Stull, W. Community environment, zoning, and the market value of single-family homes. J. Law Econ. 1975, 18, 535–557. [Google Scholar] [CrossRef]
- Alonso, W. Location and Land Use; Harvard University Press: Cambridge, MA, USA, 1964. [Google Scholar]
- Haig, R. Towards an understanding of the metropolis. Q. J. Econ. 1926, 40, 402–434. [Google Scholar] [CrossRef]
- Muth, R. Cities and Housing; University of Chicago Press: Chicago, IL, USA, 1969. [Google Scholar]
- Armstrong, R.; Rodríguez, D. An evaluation of the accessibility benefits of commuter rail in east Massachusetts using spatial hedonic price functions. Transportation 2006, 33, 21–43. [Google Scholar] [CrossRef]
- Duncan, M. The synergistic influence of light rail stations and zoning on home prices. Environ. Plan. A 2011, 43, 2125–2142. [Google Scholar] [CrossRef]
- Rosen, S. Hedonic prices and implicit markets: Product differentiation in pure competition. J. Polit. Econ. 1974, 82, 34–55. [Google Scholar] [CrossRef]
- Can, A. The measurement of neighborhood dynamics in urban house prices. Econ. Geogr. 1990, 66, 254–272. [Google Scholar] [CrossRef]
- Can, A. Specification and estimation of hedonic housing price models. Reg. Sci. Urban Econ. 1992, 22, 453–474. [Google Scholar] [CrossRef]
- Slater, P. Spatial and temporal effects in residential sales prices. J. Am. Stat. Assoc. 1973, 68, 554–561. [Google Scholar] [CrossRef]
- Slater, P. Disaggregated spatial-temporal analysis of residential sales prices. J. Am. Stat. Assoc. 1974, 69, 358–363. [Google Scholar] [CrossRef]
- Dubé, J.; Legros, D. Spatial econometrics and the hedonic pricing model: What about the temporal dimension? J. Prop. Res. 2014, 31, 333–359. [Google Scholar] [CrossRef]
- Fotheringham, A.; Crespo, R.; Yao, J. Exploring, modelling and predicting spatiotemporal variation in house prices. Ann. Reg. Sci. 2015, 54, 417–436. [Google Scholar] [CrossRef]
- Walk21. Integrating Walking + Public Transport. 2024. Available online: https://walk21.com/resources/walking-and-public-transport/ (accessed on 28 October 2024).
- Le, V.; Dannenberg, A. Moving towards physical activity targets by walking to transit: National household transportation survey: 2001–2017. Am. J. Prev. Med. 2020, 59, e115–e123. [Google Scholar] [CrossRef] [PubMed]
- Puentes, R. Washington’s Metro: Deficits by Design. 2004. Available online: https://www.brookings.edu/wp-content/uploads/2016/06/20040603_puentes.pdf (accessed on 28 October 2024).
- Institute for Transportation & Development Policy. 2024. Better Together: Walkable Cities and Public Transport. 2024. Available online: https://itdp.org/2024/08/15/better-together-walkable-cities-and-public-transport/ (accessed on 28 October 2024).
- National Association of Realtors. 2023 Community & Transportation Preference Survey. 2023. Available online: https://www.nar.realtor/infographics/2023-community-transportation-preference-survey (accessed on 28 October 2024).
- Washington Metropolitan Transit Authority. Metrorail Station Investment Strategy. 2016. Available online: https://planitmetro.com/uploads/MISIS_Report_August_2016.pdf (accessed on 28 October 2024).
- Litman, T. Evaluating Public Transit Benefits and Costs. 2024. Available online: https://www.vtpi.org/tranben.pdf (accessed on 28 October 2024).
- Guo, Y.; Peeta, S.; Somenahalli, S. The impact of walkable environment on single-family residential property values. J. Transp. Land Use 2017, 10, 1–20. [Google Scholar] [CrossRef]
- Yang, L.; Wang, B.; Zhou, J.; Wang, X. Walking accessibility and property prices. Transp. Res. D-Transp. Environ. 2018, 62, 551–562. [Google Scholar] [CrossRef]
- Petheram, S.; Nelson, A.; Miller, M.; Ewing, R. Use of the real estate market to establish light rail station catchment areas. Transp. Res. Rec. 2013, 2357, 95–99. [Google Scholar] [CrossRef]
- Gilderbloom, J.; Riggs, W.; Meares, W. Does walkability matter? An examination of walkability’s impact on housing values, foreclosures and crime. Cities 2015, 42 Pt A, 13–24. [Google Scholar] [CrossRef]
- Boyle, A.; Barrilleaux, C.; Scheller, D. Does walkability influence housing prices? Soc. Sci. Quart. 2014, 95, 852–867. [Google Scholar] [CrossRef]
- Li, W.; Joh, K.; Lee, C.; Kim, J.-H.; Park, H.; Woo, A. Assessing benefits of neighborhood walkability to single-family property values: A spatial hedonic study in Austin, Texas. J. Plan. Educ. Res. 2015, 35, 471–488. [Google Scholar] [CrossRef]
- Yang, S.; Peng, C.; Hu, S.; Zhang, P. Geospatial modelling of housing rents from TOD using MGWR and implications on integrated transportation-land use planning. Appl. Geogr. 2024, 170, 103356. [Google Scholar] [CrossRef]
- Washington Metropolitan Area Transit Authority. Time for Those Walking Shoes, Part 1. 2013. Available online: https://planitmetro.com/2013/07/30/time-for-those-walking-shoes-part-1/ (accessed on 28 October 2024).
- Environmental Systems Research Institute. ArcGIS 10.8. 2020. Available online: https://www.esri.com/en-us/arcgis/about-arcgis/overview (accessed on 28 October 2024).
- United States Environmental Protection Agency. Smart Location Database. 2021. Available online: https://www.epa.gov/smartgrowth/smart-location-mapping#SLD (accessed on 28 October 2024).
- United States Environmental Protection Agency. National Walkability Index. 2021. Available online: https://www.epa.gov/smartgrowth/smart-location-mapping#walkability (accessed on 28 October 2024).
- Federal Housing Finance Agency. Home Price Index. 2016. Available online: https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index.aspx (accessed on 28 October 2024).
- Oshan, T.; Li, Z.; Kang, W.; Wolf, L.; Fotheringham, A. MGWR: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS Int. Geo.-Inf. 2019, 8, 269. [Google Scholar] [CrossRef]
- Fotheringham, A.; Brunsdon, C.; Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships; Wiley: Hoboken, NJ, USA, 2002. [Google Scholar]
- Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle. In 2nd International Symposium on Information Theory; Petrov, B., Csáki, F., Eds.; Akadémiai Kiadó: Budapest, Hungary, 1973; pp. 267–281. [Google Scholar]
- Hurvich, C.; Simonoff, J.; Tsai, C.-L. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J. R. Stat. Soc. B 1998, 60, 271–293. [Google Scholar] [CrossRef]
- Fotheringham, A.; Yang, W.; Kang, W. Multiscale geographically weighted regression (MGWR). Ann. Am. Assoc. Geogr. 2017, 107, 1247–1265. [Google Scholar] [CrossRef]
- Breusch, T.; Pagan, A. A simple test for heteroscedasticity and random coefficient variation. Econometrica 1979, 47, 1287–1294. [Google Scholar] [CrossRef]
- Yu, H.; Fotheringham, A.; Li, Z.; Oshan, T.; Kang, W.; Wolf, L. Inference in multiscale geographically weighted regression. Geogr. Anal. 2020, 52, 87–106. [Google Scholar] [CrossRef]
- Koenker, R. A note on studentizing a test for heteroscedasticity. J. Econom. 1981, 17, 107–112. [Google Scholar] [CrossRef]
- Hamidi, S.; Bonakdar, A.; Keshavarzi, G.; Ewing, R. Do Urban Design qualities add to property values? An empirical analysis of the relationship between Urban Design qualities and property values. Cities 2020, 98, 102564. [Google Scholar] [CrossRef]
- Tukey, J. Exploratory Data Analysis; Addison-Wesley: Reading, PA, USA, 1977. [Google Scholar]
- Mennis, J. Mapping the results of geographically weighted regression. Cartogr. J. 2013, 43, 171–179. [Google Scholar] [CrossRef]
- Best, N.; Spiegelhalter, D.; Thomas, A.; Brayne, C. Bayesian analysis of realistically complex models. J. R. Stat. Soc. A Stat. 1996, 159, 323–342. [Google Scholar] [CrossRef]
- Krause, A.; Bitter, C. Spatial econometrics, land values and sustainability: Trends in real estate valuation research. Cities 2012, 29 (Suppl. S2), S19–S25. [Google Scholar] [CrossRef]
- Shiller, R. Irrational Exuberance; Princeton University Press: Princeton, NJ, USA, 2015. [Google Scholar]
- Perrins, G.; Nilsen, D. Industry Dynamics in the Washington, DC, Area: Has a Second Job Core Emerged? 2006. Available online: https://www.bls.gov/opub/mlr/2006/12/art1full.pdf (accessed on 28 October 2024).
Variable | Metro Station Buffers | Metro Service Area | |||
---|---|---|---|---|---|
Characteristic | M 1 | SD 2 | M | SD | |
Dependent | |||||
Price (USD1000.00) | 307.24 | 150.58 | 312.88 | 143.13 | |
Independent | |||||
Physical | |||||
Interior | |||||
Full baths | 2.06 | 0.75 | 2.39 | 0.74 | |
Half baths | 0.64 | 0.61 | 0.79 | 0.58 | |
Bedrooms | 3.26 | 0.83 | 3.74 | 0.79 | |
Quality | |||||
Age | 53.00 | 30.32 | 32.97 | 21.58 | |
Accessibility | |||||
Distance | 690.06 | 361.71 | 5763.46 | 4186.01 | |
Infrastructure | 4146.48 | 1419.83 | 2840.19 | 1364.82 | |
Walkability | 14.75 | 2.31 | 11.64 | 3.63 | |
Environmental | |||||
Space | |||||
Density | 5595.98 | 11,176.46 | 1444.40 | 3062.62 | |
n | 1470 | 29,422 |
Price (USD1000.00) | |||||
---|---|---|---|---|---|
Year | n | M 1 | SD 2 | Min 3 | Max 4 |
2002 | 83 | 421.78 | 195.54 | 165.00 | 950.00 |
2003 | 78 | 455.82 | 198.31 | 162.20 | 903.00 |
2004 | 137 | 453.71 | 233.61 | 162.00 | 1020.00 |
2005 | 153 | 502.85 | 216.39 | 165.00 | 1045.00 |
2006 | 139 | 467.04 | 202.81 | 180.00 | 985.00 |
2007 | 145 | 478.57 | 200.27 | 186.38 | 1010.00 |
2008 | 97 | 456.94 | 219.10 | 180.00 | 1050.00 |
2009 | 118 | 526.64 | 256.97 | 167.50 | 1056.00 |
2010 | 109 | 436.67 | 211.75 | 164.10 | 930.00 |
2011 | 89 | 470.39 | 229.34 | 165.00 | 1049.00 |
2012 | 81 | 494.33 | 233.76 | 165.00 | 1055.00 |
2013 | 124 | 510.46 | 245.88 | 165.00 | 1040.00 |
2014 | 117 | 517.37 | 247.42 | 171.00 | 1045.00 |
Total | 1470 | 478.75 | 224.76 | 186.38 5 | 1056.00 6 |
Characteristic | Coefficient 1 | SE 2 | VIF 3 | |
---|---|---|---|---|
Intercept | −167,363.47 *** | 27,951.75 | ||
Physical | ||||
Interior | Full baths | +99,604.63 *** | 5209.77 | 1.49 |
Half baths | +68,053.14 *** | 5564.58 | 1.11 | |
Bedrooms | −7219.33 | 4568.55 | 1.41 | |
Quality | ||||
Age | +986.85 *** | 121.50 | 1.32 | |
Accessibility | ||||
Distance | +15.04 * | 9.01 | 1.04 | |
Infrastructure | +12.34 *** | 2.55 | 1.28 | |
Walkability | +8369.69 *** | 1549.57 | 1.25 | |
Environmental | ||||
Space | ||||
Density | +2.25 *** | 0.31 | 1.15 | |
n AICc Adjusted R-Square Moran’s I Koenker (BP) | 1470 3583.96 0.34 +0.47 4 63.19 5 |
Characteristic | GWR 1 | MGWR 1 | ||
---|---|---|---|---|
Intercept | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo test for spatial variability (p) | 107 247.07 3.12 0.00 *** | 53 57.04 3.33 0.00 *** | |
Physical | ||||
Interior | ||||
Full baths | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo test for spatial variability (p) | 107 247.07 3.12 0.00 *** | 190 17.40 2.99 0.00 *** | |
Half baths | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo test for spatial variability (p) | 107 247.07 3.12 0.00 *** | 977 3.23 2.42 0.28 | |
Bedrooms | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo test for Spatial variability (p) | 107 247.07 3.12 0.00 *** | 927 2.93 2.39 0.12 | |
Quality | ||||
Age | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo Test for spatial variability (p) | 107 247.07 3.12 0.00 *** | 81 35.99 3.20 0.00 *** | |
Accessibility | ||||
Distance | Bandwidth Effective number of parameters Adjusted Critical t Monte Carlo Test for spatial variability (p) | 107 247.07 3.12 0.00 *** | 339 6.69 2.68 0.01 *** | |
Infrastructure | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo test for spatial variability (p) | 107 247.07 3.12 0.00 *** | 1469 1.35 2.09 0.92 | |
Walkability | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo test for spatial variability (p) | 107 247.07 3.12 0.00 *** | 142 18.98 3.03 0.00 *** | |
Environmental | ||||
Space | ||||
Density | Bandwidth Effective number of parameters Adjusted critical t Monte Carlo test for spatial variability (p) | 107 247.04 3.12 0.00 * | 1218 1.30 2.07 0.87 |
GWR | MGWR | ||||
---|---|---|---|---|---|
Characteristic | M 1 | SD 2 | M | SD | |
Intercept | +0.02 | 0.71 | −0.002 | 0.73 | |
Physical | |||||
Interior | |||||
Full baths | +0.26 | 0.16 | +0.28 | 0.13 | |
Half baths | +0.14 | 0.10 | +0.16 | 0.02 | |
Bedrooms | +0.07 | 0.11 | +0.07 | 0.03 | |
Quality | |||||
Age | +0.01 | 0.19 | +0.02 | 0.16 | |
Accessibility | |||||
Distance | +0.01 | 0.15 | +0.004 | 0.08 | |
Infrastructure | +0.05 | 0.10 | +0.04 | 0.003 | |
Walkability | +0.02 | 0.13 | +0.02 | 0.10 | |
Environmental | |||||
Space | |||||
Density | +0.10 | 0.56 | +0.01 | 0.01 | |
n Effective number of parameters AICc Adjusted R-Square Moran’s I | 1470 247.07 2437.37 0.75 +0.01 3 | 1470 144.91 2266.79 0.76 −0.01 4 |
Characteristic | GWR | MGWR | |
---|---|---|---|
Accessibility | |||
Distance | Min 1 p25 2 p50 3 p75 4 Max 5 | −0.37 −0.08 +0.01 +0.08 +0.56 | −0.17 −0.01 +0.02 +0.04 +0.18 |
Infrastructure | Min p25 p50 p75 Max | −0.24 −0.01 +0.04 +0.09 +0.35 | +0.03 +0.03 +0.04 +0.04 +0.04 |
Walkability | Min p25 p50 p75 Max | −0.43 −0.05 +0.01 +0.09 +0.41 | −0.21 −0.04 +0.02 +0.08 +0.31 |
n | 1470 | 1470 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zolnik, E. Capturing the Value of Walkability. Future Transp. 2024, 4, 1334-1349. https://doi.org/10.3390/futuretransp4040064
Zolnik E. Capturing the Value of Walkability. Future Transportation. 2024; 4(4):1334-1349. https://doi.org/10.3390/futuretransp4040064
Chicago/Turabian StyleZolnik, Edmund. 2024. "Capturing the Value of Walkability" Future Transportation 4, no. 4: 1334-1349. https://doi.org/10.3390/futuretransp4040064
APA StyleZolnik, E. (2024). Capturing the Value of Walkability. Future Transportation, 4(4), 1334-1349. https://doi.org/10.3390/futuretransp4040064