The Impact of Compact and Mixed Development on Land Value: A Case Study of Richmond, Virginia
Abstract
:1. Introduction
1.1. Study Aim and Scope
1.2. Study Area and Geographic Unit of Analysis
2. Materials and Methods
2.1. Land Value—The Dependent Varialbe
2.2. Development Compactness (D_Compact)
2.3. Land Use Mix (LU_Mix)
2.4. Accessibility to Jobs (A_Jobs)
2.5. Accessibility to Retail Stores (A_Retail)
2.6. Accessibility to Public Transportation (A_Bus)
3. Results
3.1. Correlation Analysis
3.2. Collinearity Diagnostics
3.3. Regression Analysis 1: Impacts of Development Pattern on Land Value
3.4. Regression Analysis 2: Impacts of Development Pattern and Accessibility on Land Value
3.5. Regression Analysis 3: Sub-Groups Comparison
4. Discussion
4.1. Model 1—Low Land Value Group
4.2. Model 2—Medium Land Value Group
4.3. Model 3—High Land Value Group
4.4. Implications
- In general, accessibility has greater impacts than development pattern on land value.
- Compact development and mixed land use influence land value differently depending on the nature of existing land uses and land values.
- Accessibility to jobs and retail stores always contributes to the increase of land value. On the other hand, accessibility to public transportation helps but it does not influence land value in a consistent fashion.
- A sweet spot seems to be an area that has higher residential property values, good proximity to the city center, less industrial land uses, and of course good access to jobs and retail stores.
- There are a number of candidate Census Block Groups to consider if Richmond wants to promote compact development and mixed land use and benefit from the potential of increased land value. For example, Census Block Groups located south of the I-195 (the segment running east-west) and north of the James River (see Figure 11) have comparable accessibility to the ones in the medium land value group. Similarly, Census Block Groups southwest of I-64/I-95 and east of the I-195 (the segment running north-south) are potential candidates as well. On the other hand, it will be a challenge to expand further southward due to the James River, a major natural barrier, and that connections to the city center is limited by only a handful of bridges.
4.5. Limintations and Future Research
- As a case study, the City of Richmond is studied in isolation when in fact development pattern and accessibility do not stop at city boundaries. For example, there are jobs and retail stores outside of the City that are accessible but not included in the study. If the study area is expanded to the entire Richmond MSA for example (provided that the needed data are available), more can be learned about land value and its interactions with development pattern and accessibility across jurisdictions or among urban, suburban, and rural areas.
- As a cross-sectional study, this study only offers a “snapshot” picture of a given point in time. However, there may be a time lag before certain impacts can take place. Furthermore, impacts may vary depending on how long or short the time lag is. For this reason, a longitudinal study would be better suited to investigate the lag effects of independent variables on the dependent variable, and even the latent interactions and mutual dependencies among variables over time.
- All jobs are not created equal. This study takes into account the quantity of jobs when assessing the accessibility to jobs. It would be a worthy effort to take a deeper dive and break accessibility to jobs by the types of jobs, their wage bands and develop a more robust accessibility measure.
- Regarding the independent variable land value, it would be interesting to find out if and to what extent assessed value differs from market value, and how they react to development pattern and accessibility.
- Lastly, this study examines the impacts of compact and mixed development on land value (along with accessibility). In the pursuit of a more sustainable future, social and environmental impacts resulted from such development patterns can be further explored.
5. Conclusions
Acknowledgments
Conflicts of Interest
References
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Land Use Category | Description | Number of Parcels | Percent |
---|---|---|---|
Residential | single-family, townhouse, duplex, apartments | 55,678 | 89.12% |
Business | office, commercial, mixed-commercial | 4407 | 7.05% |
Industrial | industrial | 1160 | 1.86% |
Public | government, education, religion | 1229 | 1.97% |
Developed parcels: | 62,474 | 100.00% | |
Undeveloped parcels: | 7313 | ||
Total: | 69,787 |
NAICS Sector | NAICS Sector Description | Number of Establishments | Percent of Establishments | Number of Employees | Percent of Employees |
---|---|---|---|---|---|
11 | Agriculture, Forestry, Fishing and Hunting | 0 | 0.00% | 0 | 0.00% |
21 | Mining, Quarrying, and Oil and Gas Extraction | 3 | 0.05% | 29 | 0.02% |
22 | Utilities | 13 | 0.21% | 801 | 0.53% |
23 | Construction | 390 | 6.44% | 7403 | 4.93% |
31–33 | Manufacturing | 194 | 3.21% | 5829 | 3.88% |
42 | Wholesale Trade | 254 | 4.20% | 3614 | 2.41% |
44–45 | Retail Trade | 646 | 10.67% | 7688 | 5.12% |
48–49 | Transportation and Warehousing | 124 | 2.05% | 3905 | 2.60% |
51 | Information | 104 | 1.72% | 1972 | 1.31% |
52 | Finance and Insurance | 314 | 5.19% | 7601 | 5.06% |
53 | Real Estate and Rental and Leasing | 230 | 3.80% | 1464 | 0.98% |
54 | Professional, Scientific, and Technical Services | 808 | 13.35% | 10,136 | 6.75% |
55 | Management of Companies and Enterprises | 102 | 1.69% | 8788 | 5.86% |
56 | Administrative and Support and Waste Management and Remediation Services | 257 | 4.25% | 7150 | 4.76% |
61 | Educational Services | 79 | 1.31% | 18,312 | 12.20% |
62 | Health Care and Social Assistance | 592 | 9.78% | 25,281 | 16.84% |
71 | Arts, Entertainment, and Recreation | 119 | 1.97% | 4487 | 2.99% |
72 | Accommodation and Food Services | 490 | 8.10% | 9445 | 6.29% |
81 | Other Services (except Public Administration) | 495 | 8.18% | 4676 | 3.12% |
92 | Public Administration | 839 | 13.86% | 21,503 | 14.33% |
Total | 6053 | 100.00% | 150,084 | 100.00% |
Variable | Land_Value | D_Compact | LU_Mix | A_Jobs | A_Retail | A_Bus |
---|---|---|---|---|---|---|
Land_Value | 1.000 | 0.217 | −0.042 | 0.734 | 0.480 | 0.524 |
D_Compact | 0.217 | 1.000 | −0.147 | 0.310 | 0.036 | 0.485 |
LU_Mix | −0.042 | −0.147 | 1.000 | −0.084 | −0.094 | −0.118 |
A_Jobs | 0.734 | −0.310 | −0.084 | 1.000 | 0.315 | 0.851 |
A_Retail | 0.480 | 0.036 | −0.094 | 0.315 | 1.000 | 0.139 |
A_Bus | 0.524 | 0.485 | −0.118 | 0.851 | 0.139 | 1.000 |
Dimension | ||||||||
---|---|---|---|---|---|---|---|---|
Independent Variable | Tolerance | 1 | 2 | 3 | 4 | 5 | 6 | |
Condition Index | 1.000 | 5.864 | 8.134 | 10.545 | 21.144 | 28.456 | ||
Variance Proportion | ||||||||
Constant | 0.000 | 0.002 | 0.001 | 0.056 | 0.532 | 0.409 | ||
D_Compact | 0.718 | 0.003 | 0.178 | 0.631 | 0.002 | 0.130 | 0.057 | |
LU_Mix | 0.967 | 0.003 | 0.409 | 0.163 | 0.280 | 0.100 | 0.044 | |
A_Jobs | 0.224 | 0.000 | 0.003 | 0.041 | 0.038 | 0.211 | 0.706 | |
A_Retail | 0.831 | 0.001 | 0.001 | 0.013 | 0.162 | 0.331 | 0.492 | |
A_Bus | 0.206 | 0.000 | 0.006 | 0.013 | 0.050 | 0.202 | 0.727 | |
Dependent Variable: | Land_Value: average assessed land value per square foot | |||||||
Independent Variables: | D_Compact: development compactness index | |||||||
LU_Mix: land use mix index | ||||||||
A_Jobs: accessibility to jobs index | ||||||||
A_Retail: accessibility to retail stores index | ||||||||
A_Bus: accessibility to public transportation index |
Independent Variable | Coefficient | Std. Error | beta | t 1 | Sig. 1 |
---|---|---|---|---|---|
Constant | 2.495 | 3.168 | 0.788 | 0.432 | |
D_Compact | 1.040 | 0.378 | 0.216 | 2.748 | 0.007 |
LU_Mix | −0.035 | 0.275 | −0.010 | −0.126 | 0.900 |
Dependent Variable: | Land_Value: average assessed land value per square foot | ||||
Independent Variables: | D_Compact: development compactness index | ||||
LU_Mix: land use mix index | |||||
Multiple R = 0.217 | R2 = 0.047 | Adjusted R2 = 0.035 | F 1 = 3.919 (Sig. 1 = 0.022) |
Independent Variable | Coefficient | Std. Error | beta | t 1 | Sig. 1 |
---|---|---|---|---|---|
Constant | −31.164 | 3.912 | −7.966 | 0.000 | |
D_Compact | 0.418 | 0.278 | 0.087 | 1.503 | 0.135 |
LU_Mix | 0.111 | 0.174 | 0.032 | 0.638 | 0.524 |
A_Jobs | 0.344 | 0.039 | 0.903 | 8.734 | 0.000 |
A_Retail | 0.932 | 0.208 | 0.240 | 4.469 | 0.000 |
A_Bus | −0.018 | 0.006 | −0.315 | −2.929 | 0.004 |
Dependent Variable: | Land_Value: average assessed land value per square foot | ||||
Independent Variables: | D_Compact: development compactness index | ||||
LU_Mix: land use mix index | |||||
A_Jobs: accessibility to jobs index | |||||
A_Retail: accessibility to retail stores index | |||||
A_Bus: accessibility to public transportation index | |||||
Multiple R = 0.794 | R2 = 0.630 | Adjusted R2 = 0.618 | F 1 = 52.707 (Sig. 1 = 0.000) |
Overall Model | Model 1 | Model 2 | Model 3 | |
---|---|---|---|---|
(N = 161) | (N = 125) | (N = 26) | (N = 10) | |
Land_Value range | $0.84–$59.61 | $0.84–$8.68 | $8.69–$26.91 | $26.92–$59.61 |
($ per square foot) | ||||
Independent Variable | Standardized Regression Coefficient (beta) | |||
D_Compact | 0.087 | −0.064 | 0.176 | −0.361 |
LU_Mix | 0.032 | −0.125 | 0.181 | 0.206 |
A_Jobs | 0.903 | 0.186 | 0.465 | 0.899 |
A_Retail | 0.240 | 0.328 | 0.495 | 0.063 |
A_Bus | −0.315 | 0.214 | 0.095 | −0.987 |
R2 = 0.630 | R2 = 0.284 | R2 = 0.556 | R2 = 0.633 |
Variable | Overall Model | Model 1 | Model 2 | Model 3 | |
Land_Value | Min. | 0.842 | 0.842 | 8.685 | 29.547 |
Max. | 59.608 | 8.390 | 26.913 | 59.608 | |
Mean | 7.517 | 3.162 | 16.345 | 39.010 | |
S.D. | 10.007 | 1.766 | 5.236 | 8.400 | |
D_Compact | Min. | 1.385 | 1.385 | 2.838 | 2.980 |
Max. | 13.712 | 13.712 | 8.727 | 8.164 | |
Mean | 5.088 | 4.844 | 5.786 | 6.318 | |
S.D. | 2.076 | 2.106 | 1.784 | 1.663 | |
LU_Mix | Min. | 0.000 | 0.000 | 2.398 | 4.001 |
Max. | 14.411 | 14.411 | 13.016 | 13.702 | |
Mean | 7.728 | 7.733 | 7.932 | 7.133 | |
S.D. | 2.856 | 2.831 | 2.862 | 3.356 | |
A_Jobs | Min. | 55.221 | 55.221 | 84.791 | 121.774 |
Max. | 227.242 | 145.726 | 156.768 | 227.242 | |
Mean | 93.017 | 84.016 | 115.321 | 147.535 | |
S.D. | 26.248 | 18.104 | 17.967 | 31.162 | |
A_Retail | Min. | 11.615 | 11.615 | 14.030 | 15.629 |
Max. | 32.721 | 20.807 | 32.721 | 20.870 | |
Mean | 16.031 | 15.275 | 18.791 | 18.298 | |
S.D. | 2.574 | 1.840 | 3.437 | 1.641 | |
A_Bus | Min. | 246.361 | 246.361 | 404.025 | 771.350 |
Max. | 1082.808 | 1082.808 | 1016.813 | 1050.317 | |
Mean | 613.301 | 570.271 | 728.187 | 852.467 | |
S.D. | 172.039 | 155.327 | 146.432 | 82.477 |
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Suen, I.-S. The Impact of Compact and Mixed Development on Land Value: A Case Study of Richmond, Virginia. Urban Sci. 2018, 2, 47. https://doi.org/10.3390/urbansci2020047
Suen I-S. The Impact of Compact and Mixed Development on Land Value: A Case Study of Richmond, Virginia. Urban Science. 2018; 2(2):47. https://doi.org/10.3390/urbansci2020047
Chicago/Turabian StyleSuen, I-Shian (Ivan). 2018. "The Impact of Compact and Mixed Development on Land Value: A Case Study of Richmond, Virginia" Urban Science 2, no. 2: 47. https://doi.org/10.3390/urbansci2020047