Development of Land Price Model with Geographically Weighted Regression on the Existence of Spatial Planning Zones: A Case Study in the Eastern Bandung City

Albertus Deliar, Dzikri Nashrul Jabbaaar, Alfita Puspa Handayani

Abstract


One of the methods used to estimate land prices is the Geographically Weighted Regression (GWR). The GWR method is built based on the dependent and independent variables (land prices) (the spatial proximity between the land object and other facilities). However, this study will develop the independent variable by adding a spatial planning zone to provide the complexity of land price estimation. This study proposes an implementation mechanism by setting each zone type as an independent variable. Based on the spatial planning zones in Eastern Bandung City, there are five spatial planning zones. Thus, 15 variables were used in this GWR model, with ten variables from public facilities and five from spatial planning zones. The variables are categorized into worship, industry, government offices, health, sports/recreation, education, prisons, defense offices, terminals, trade and service zones, industrial zones, and low-residential, medium, and high-residential zones. The results of this study indicate that the implementation of the spatial planning zone variable has a better accuracy rate than the GWR model without involving the spatial planning zone variable. The approach with the proposed mechanism gives better accuracy of 8.6%. Spatial planning zone variable can be a new perspective in making a GWR-based land price estimation model in addition to the physical object variable in the form of public or social facilities, especially to improve the quality of the model formed.

Keywords


GWR; spatial planning zone; determining variable; eastern Bandung city

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References


G. Fedele, C. I. Donatti, I. Bornacelly, and D. G. Hole, “Nature-dependent people: Mapping human direct use of nature for basic needs across the tropics,†Glob. Environ. Chang., vol. 71, p. 102368, 2021.

G. Jin, K. Chen, P. Wang, B. Guo, Y. Dong, and J. Yang, “Trade-offs in land-use competition and sustainable land development in the North China Plain,†Technol. Forecast. Soc. Change, 2019, doi: 10.1016/j.techfore.2019.01.004.

A. P. Handayani, A. Deliar, I. Sumarto, and I. Syabri, “Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model,†Indones. J. Geogr., 2020, doi: 10.22146/ijg.43724.

B. Glumac, M. Herrera-Gomez, and J. Licheron, “A hedonic urban land price index,†Land use policy, vol. 81, pp. 802–812, 2019.

Liu, Y. Meng, and J. Ma, “Spatial Distribution of Influence Factors of Residential Land Price in Cangzhou City Based on GWR Model,†2019, doi: 10.1088/1755-1315/332/2/022032.

J. Gyourko, J. S. Hartley, and J. Krimmel, “The local residential land use regulatory environment across US housing markets: Evidence from a new Wharton index,†J. Urban Econ., vol. 124, p. 103337, 2021.

B. F. Richardson, “The price we pay for land: the political economy of Pukekohe’s development,†J. New Zeal. Pacific Stud., vol. 9, no. 1, pp. 7–23, 2021.

Y. Kang et al., “Understanding house price appreciation using multi-source big geo-data and machine learning,†Land use policy, vol. 111, p. 104919, 2021.

F. Yuan, Y. D. Wei, and J. Wu, “Amenity effects of urban facilities on housing prices in China: Accessibility, scarcity, and urban spaces,†Cities, vol. 96, p. 102433, 2020.

S. Sisman and A. C. Aydinoglu, “A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul,†Land use policy, vol. 119, p. 106183, 2022.

A. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. 2002.

N. Liu and J. Strobl, “Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M) GWR model,†Big Earth Data, pp. 1–24, 2022.

J. You, X. Ding, M. Niño-Zarazúa, and S. Wang, “The intergenerational impact of house prices on education: evidence from China,†J. Hous. Econ., vol. 54, p. 101788, 2021.

Z. Yang, C. Li, and Y. Fang, “Driving factors of the industrial land transfer price based on a geographically weighted regression model: Evidence from a rural land system reform pilot in China,†land, vol. 9, no. 1, p. 7, 2020.

M. Goldberg, P. Kugler, and F. Schär, “Land Valuation in the Metaverse: Location Matters,†SSRN Electron. J., 2021, doi: 10.2139/ssrn.3932189.

E. Guliker, E. Folmer, and M. van Sinderen, “Spatial Determinants of Real Estate Appraisals in The Netherlands: A Machine Learning Approach,†ISPRS Int. J. Geo-Information, vol. 11, no. 2, 2022, doi: 10.3390/ijgi11020125.

A. Ebekozien, “A qualitative approach to investigate low-cost housing policy provision in Edo State, Nigeria,†Int. Plan. Stud., vol. 26, no. 2, pp. 165–181, 2021.

D.-G. Owusu-Manu, D. J. Edwards, K. A. Donkor-Hyiaman, R. O. Asiedu, M. R. Hosseini, and E. Obiri-Yeboah, “Housing attributes and relative house prices in Ghana,†Int. J. Build. Pathol. Adapt., 2019.

R. Trojanek, J. Tanaś, and M. Trojanek, “The effect of perpetual usufruct on single-family house prices in Poznań,†J. Int. Stud., vol. 12, no. 3, 2019.

Y. Freemark, “Upzoning Chicago: Impacts of a Zoning Reform on Property Values and Housing Construction,†Urban Aff. Rev., 2020, doi: 10.1177/1078087418824672.

S. Hu, S. Yang, W. Li, C. Zhang, and F. Xu, “Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China,†Appl. Geogr., vol. 68, pp. 48–56, 2016, doi: 10.1016/j.apgeog.2016.01.006.

G. P. R. Rynjani and R. Haryanto, “Kajian Harga Tanah dan Penggunaan Lahan di Kawasan Perdagangan dan Jasa Kelurahan Lamper Kidul, Kota Semarang,†Tek. Perenc. Wil. Kota, vol. Volume 4 N, pp. 417–427, 2015.

Y. Wu, X. Zhang, M. Skitmore, Y. Song, and E. C. M. Hui, “Industrial land price and its impact on urban growth: A Chinese case study,†Land use policy, vol. 36, no. 199–209, 2014, doi: 10.1016/j.landusepol.2013.08.015.

Kementerian ATR/BPN, “Penelitian Pemanfaatan Zona Nilai Tanah Berbasis Penataan Ruang,†2015.

Nandi and V. R. Dewiyanti, “Urban Sprawl Development in Eastern Bandung Region,†IOP Conf. Ser. Earth Environ. Sci., vol. 286, no. 1, pp. 1–8, 2019, doi: 10.1088/1755-1315/286/1/012031.

N. R. Sukatmadiredja, “The Effect of Buying Decisions on Mutiara City Housing in Banjarbendo Village, Sidoarjo Regency,†Int. J. Econ. Bus. Account. Res., vol. 6, no. 2, 2022.

P. Bhardwaj, “Types of sampling in research,†J. Pract. Cardiovasc. Sci., vol. 5, no. 3, p. 157, 2019.

M. S. Mahmud, J. Z. Huang, S. Salloum, T. Z. Emara, and K. Sadatdiynov, “A survey of data partitioning and sampling methods to support big data analysis,†Big Data Min. Anal., vol. 3, no. 2, pp. 85–101, 2020.

C. A. Ramezan, T. A. Warner, and A. E. Maxwell, “Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification,†Remote Sens., vol. 11, no. 2, p. 185, 2019.

R. T. Walker, “Geography, Von Thünen, and Tobler’s First Law: Tracing the Evolution of A Concept,†Geogr. Rev., vol. 112, no. 4, pp. 591–607, 2022.

J. Mohamed, “Time series modeling and forecasting of Somaliland consumer price index: a comparison of ARIMA and regression with ARIMA errors,†Am. J. Theor. Appl. Stat., vol. 9, no. 4, pp. 143–153, 2020.

A. Comber et al., “A route map for successful applications of geographically weighted regression,†Geogr. Anal., 2022.

U. Bansal, A. Narang, A. Sachdeva, I. Kashyap, and S. P. Panda, “Empirical analysis of regression techniques by house price and salary prediction,†in IOP Conference Series: Materials Science and Engineering, 2021, vol. 1022, no. 1, p. 12110.




DOI: http://dx.doi.org/10.18517/ijaseit.13.4.18126

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