The evaluation of real estate investment environment constitutes a critical research domain for investment decision-making and regional economic development. This paper systematically reviews the progress in real estate investment environment assessment, with particular emphasis on the application of diverse statistical methodologies. Through comprehensive literature analysis, we identify that existing evaluation systems primarily construct indicator frameworks across five dimensions: macroeconomic conditions, policy regulations, market supply-demand dynamics, infrastructure, and social environment, employing quantitative techniques including factor analysis, regression modeling, and spatial econometrics. The comparative analysis examines the applicability, advantages, and limitations of various statistical models, with special focus on panel data models and machine learning applications in dynamic assessment. The findings demonstrate that the evolution from static analysis to dynamic prediction in real estate investment evaluation has been significantly enhanced through methodological innovations in statistics. The paper concludes by identifying current limitations in data quality and model interpretability, while proposing directions for future research.
Borui Li (Wed,) studied this question.
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