住宅小区全景画像分析算法模型

通过自主研发不动产大数据 AI 深度分析算法，结合特征价格模型进行多维 度属性因素修正，通过对全国小区楼盘大字典中小区个别因素年代、楼层、户型、朝向等）、区域因素（交通、学校、公共服务配套等），交易行为因素（换手率、挂牌量等），使用特征价格模型（Hedonic）确定影响不动产价格变化的因素，修正最优拟合模型，从价格、内部物业属性、外部城市环 境属性以及市场交易行为属性，实现了更加准确、高效、便捷地进行不动产价值全方位、多维度、穿透式分析以及精准定价。

By applying the independently developed in-depth analysis algorithm for real estate big data AI, combined with the Hedonic Price Model for multi-dimensional attribute correction, this dataset integrates a wide range of factors sourced from the national dictionary of residential communities and real estate projects, including individual community attributes (e.g., construction age, floor level, apartment layout, orientation, etc.), regional attributes (traffic conditions, schools, public service supporting facilities, etc.), and transaction behavior attributes (turnover rate, listing volume, etc.). The Hedonic Price Model is used to determine the factors influencing real estate price fluctuations, and the optimally fitted model is calibrated accordingly. By covering four dimensions including property price, internal property attributes, external urban environmental attributes and market transaction behavior attributes, this dataset enables accurate, efficient and convenient all-round, multi-dimensional penetrating analysis and precise pricing of real estate values.

搜集汇总

背景与挑战

背景概述

该数据集是一个住宅小区全景画像分析算法模型，通过自主研发的不动产大数据AI深度分析算法，结合特征价格模型对小区多维度属性因素（如内部物业、外部环境、市场交易行为）进行修正和优化。它旨在实现不动产价值的全方位、多维度、穿透式分析，提供更加准确、高效和便捷的精准定价功能。