[1] Critical factors determining rental value of residential property in Ibadan metropolis, Nigeria. Property Management, 32, 224-240(2014).

[2] Mass appraisal of residential apartments: An application of random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39, 1772-1778(2012).

[3] The transition of land to urban use. Journal of Political Economy, 87, 161-169(1979).

[4] Predicting house prices with spatial dependence: A comparison of alternative methods. Journal of Real Estate Research, 32, 139-159(2010).

[5] Random forests. Machine Learning, 45, 5-32(2001).

[6] Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47, 431-443(1998).

[7] et alMachine learning and the spatial structure of house prices and housing returns. ID 1316046, SSRN Scholarly Paper, 14 December. Rochester, NY: Social Science Research Network(2008). https://papers.ssrn.com/abstract=1316046

[8] The fundamentals of land prices and urban growth. Journal of Urban Economics, 26, 295-306(1989). https://linkinghub.elsevier.com/retrieve/pii/009411908990003X

[9] Urban growth and the price of land. University of British Columbia, Faculty of Commerce and Business Administration(1986).

[10] et alUrban land value maps: A methodological approach. Geodetski Vestnik, 58, 535-551(2014). WOS:000344633400007

[11] Prediction of housing location price by a multivariate spatial method: Cokriging. Journal of Real Estate Research, 29, 91-114(2007).

[12] Estimation of housing price variations using spatio-temporal data. Sustainability, 11, 1551(2019).

[13] Which school attributes matter? The influence of school district performance and demographic composition on property values. Journal of Urban Economics, 63, 451-466(2008).

[14] Local hedonic house-price modelling for urban planners: Advantages of using local regression techniques. Environment and Planning B: Planning and Design, 40, 664-682(2013).

[15] Onshore wind farm suitability analysis using GIS-based analytic hierarchy process: A case study of Fukushima Prefecture, Japan. Geoinformatics & Geostatistics: An Overview(2018).

[16] Neighborhood parks and residential property values in Greenville, South Carolina. Journal of Agricultural and Applied Economics, 33, 487-492(2001).

[17] Housing price forecasting based on genetic algorithm and support vector machine. Expert Systems with Applications, 38, 3383-3386(2011).

[18] Moving window kriging with geographically weighted variograms. Stochastic Environmental Research and Risk Assessment, 24, 1193-1209(2010).

[19] A Practical Guide to Geostatistical Mapping. Hengl(2009).

[20] About regression-kriging: From equations to case studies.. Computers & Geosciences, 33, 1301-1315(2007).

[21] Prediction of the purchase cost of agricultural land: The example of Côte-d’Or, France. Land Use Policy, 52, 464-476(2016). https://linkinghub.elsevier.com/retrieve/pii/S0264837716000065

[22] et alSpatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China. Applied Geography, 68, 48-56(2016).

[23] Visualization of spatial distribution and temporal change of land prices for residential use in Tokyo 23 wards using spatio-temporal kriging. In: Proceedings of 10th International Conference on Computers in Urban Planning and Urban Management, 1-11(2007).

[24] The impact of a high‐speed railway on residential land prices. Papers in Regional Science, 97, 1305-1335(2018).

[25] Estimating the residential land damage of the Fukushima nuclear accident. Journal of Urban Economics, 99, 148-160(2017).

[26] Location, location, location: The 3L Approach to house price determination. Journal of Housing Economics, 17, 175-190(2008).

[27] A study on estimation of land value using spatial statistics: Focusing on real transaction land prices in Korea. Sustainability, 8, 203(2016). http://www.mdpi.com/2071-1050/8/3/203

[28] Land use regulations and the value of land and housing: An intra-metropolitan analysis. Journal of Urban Economics, 81, 136-148(2014). http://dx.doi.org/10.1016/j.jue.2014.03.004

[29] A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Southern African Institute of Mining and Metallurgy, 52, 119-139(1951).

[30] Building predictive models in R using the caret package. Journal of Statistical Software, 28, 1-26(2008).

[31] Applied Predictive Modeling.. New York: Springer-Verlag(2013). https://www.springer.com/gp/book/9781461468486

[32] Geostatistical mapping of real estate prices: An empirical comparison of kriging and cokriging. International Journal of Geographical Information Science, 28, 1904-1921(2014).

[33] et alUrban residential land value analysis: Case Danyang, China. Geo-spatial Information Science, 10, 228-234(2007).

[34] Real estate and land price models for the Greater Zurich application of UrbanSim. Working Paper(2006). https://www.ncbi.nlm.nih.gov/pubmed/12233488

[35] A geostatistical modeling of urban land values in Milwaukee, Wisconsin. Geographic Information Sciences, 10, 49-57(2004).

[36] Statistical Handbook of Japan. Statistics Bureau Ministry of Internal Affairs and Communications Japan(2016). http://www.stat.go.jp/english/data/ handbook/pdf/ 2016all.pdf

[37] A spatial econometric analysis of residential land prices in Kuwait. Regional Studies, Regional Science, 5, 290-311(2018).

[38] The Government Land Sales programme and developers’ willingness to pay for accessibility in Singapore, 1990-2015. Land Use Policy, 75, 292-302(2018).

[39] The effect of nuclear accidents on land prices: Evidence from Fukushima-Daiichi in Japan. Rochester, NY: Social Science Research Network(2017). https://papers.ssrn.com/abstract=3057221

[40] et alThe residential real estate market in Italy: A spatio-temporal analysis. Quality & Quantity, 53, 2451-2472(2019).

[41] Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42, 2928-2934(2015).

[42] Multivariable geostatistics in S: The gstat package. Computers & Geosciences, 30, 683-691(2004).

[43] et alSpatial data analysis and mapping using machine learning algorithms. doi, Ltd, 95-148(2010).

[44] Generalized boosted models: A guide to the gbm package(2005).

[45] Forecasting the land price using statistical and neural network software. 3rd International Conference on Recent Trends in Computing, 112-121(2015).

[46] Hedonic price function for residential area focusing on the reasons for residential preferences in Japanese metropolitan areas. Journal of Risk and Financial Management, 11, 39(2018).

[47] et alSpatial modeling and geovisualization of rental prices for real estate portals. Computational Science and Its Applications - ICCSA 2016, 120-133. Lecture Notes in Computer Science. Springer International Publishing(2016).

[48] et alEstimating quality adjusted commercial property price indexes using Japanese REIT data. Journal of Property Research, 32, 217-239(2015).

[49] Pricing structure in Tokyo metropolitan land markets and its structural changes: Pre-bubble, bubble, and post-bubble periods. The Journal of Real Estate Finance and Economics, 35, 475-496(2007). http://link.springer.com/10.1007/s11146-007-9052-8

[50] Regression-based air temperature spatial prediction models: An example from Poland. Meteorologische Zeitschrift, 577-585(2013).

[51] Impact of a disaster on land price: Evidence from Fukushima nuclear power plant accident. The Singapore Economic Review, 61, 1640003(2016).

[52] Environmental management framework for wind farm siting: Methodology and case study. Journal of Environmental Management, 91, 2134-2147(2010). https://www.ncbi.nlm.nih.gov/pubmed/20541310

[53] A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234-240(1970). https://www.jstor.org/stable/143141?origin=crossref

[54] Changes in the factors influencing public acceptance of nuclear power generation in Japan since the 2011 Fukushima Daiichi nuclear disaster. Risk Analysis, 36, 98-113(2016). https://www.ncbi.nlm.nih.gov/pubmed/26224041

[55] 2011. Land price maps of Tokyo Metropolitan Area. Procedia Social and Behavioral Sciences 21. International Conference: Spatial Thinking and Geographic Information Sciences, 193-202(2011).

[56] et alGIS-based approach for municipal renewable energy planning to support post-earthquake revitalization: A Japanese case study. Sustainability, 8, 703(2016).

[57] et alReal estate price forecasting based on SVM optimized by PSO. Optik, 125, 1439-1443(2014).

[58] Relationship between urban land price and housing price: Evidence from 21 provincial capitals in China. Habitat International, 40, 9-17(2013).

[59] et alCompetitive intensity, developer expectation, and land price: Evidence from Hangzhou, China. Journal of Urban Planning and Development, 144, 04018040(2018). https://www.ncbi.nlm.nih.gov/pubmed/30906108

[60] The immediate impact of the Fukushima Daiichi accident on local property values. Risk Analysis, 33, 2023-2040(2013). https://www.ncbi.nlm.nih.gov/pubmed/23557167

[61] Effects of land and building usage on population, land price and passengers in station areas: A case study in Fukuoka, Japan. Frontiers of Architectural Research, 3, 199-212(2014). https://linkinghub.elsevier.com/retrieve/pii/S2095263514000053