贵阳市各区域二手房转移登记标杆项目识别数据集
收藏资源简介：
1、数据采集：从不动产登记系统中，依法合规采集贵阳市各区县二手房成交数据（以2025年4月-2025年9月的数据为例）。原始数据包括各楼盘项目的成交套数、成交面积、所在行政区划、项目名称等字段。采集周期为月度，确保数据时效性与代表性。 2、数据处理：1）以“项目名称”为关键标识，对多源数据进行清洗与归一化，统一命名规则；2）按行政区划（区域）进行分组统计，计算每个项目的累计成交套数与累计成交面积；3）设定筛选阈值：仅保留“成交面积排名前10”的活跃项目，形成“标杆项目清单”；4）生成结构化、标准化的“标杆项目识别数据集”，包含“区域”“项目名称”“成交套数”“成交面积”等关键指标。 3、数据应用：输出高质量的标杆项目榜单，用于发布《贵阳市二手房市场月报》、支持城市更新项目选址、辅助开发商判断新盘布局、指导购房者识别市场热点区域、为金融信贷机构提供抵押物价值评估依据及服务政府开展房地产市场运行监测与预警。
1. Data Collection: Legally and compliantly collect second-hand housing transaction data of each district and county in Guiyang from the real estate registration system, taking the data from April 2025 to September 2025 as an example. The raw data covers fields including the number of transaction units, transaction area, administrative division where the project is located, and project name of each real estate project. The collection is conducted on a monthly basis to ensure data timeliness and representativeness. 2. Data Processing: 1) Take "project name" as the key identifier to clean, normalize and unify the naming rules for multi-source data; 2) Conduct grouped statistics according to administrative divisions (regions), and calculate the cumulative number of transaction units and cumulative transaction area of each project; 3) Set screening thresholds: only retain the top 10 active projects in terms of transaction area to form a "benchmark project list"; 4) Generate a structured and standardized "benchmark project identification dataset" containing key indicators such as "region", "project name", "number of transaction units" and "transaction area". 3. Data Application: Output high-quality benchmark project lists, which are used to release the "Guiyang Second-hand Housing Market Monthly Report", support site selection for urban renewal projects, assist developers in judging new housing layout, guide homebuyers to identify market hotspots, provide basis for financial credit institutions to evaluate collateral value, and serve the government in carrying out real estate market operation monitoring and early warning.
