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基于xgr-stacking模型的存量住房价格预测研究
首发时间：2024-06-27
摘要：我国目前已经进入存量住房时代，随着上海、南京、杭州等城市的存量住房交易价格相继被隐藏，导致市场信息的不对称，使交易变的更加复杂和困难，而房地产行业的"以旧换新"政策又鼓励居民出售存量住房并购买新房，因此准确预测存量住房价格显得尤为重要。目前机器学习被广泛用来做房价预测，但使用的模型大多为单一模型，而单一模型容易出现过拟合、表达能力欠缺等局限性。为了克服这些缺点，利用南京市的存量住房交易数据为基础，使用关联度分析选出对成交价格影响最大的12个因素，建立xgboost模型、随机森林模型、gbdt模型这三个单一模型和xgr-stacking这一融合模型，以均方误差mse和决定系数r2作为评判依据，结果显示，xgr-stacking模型的决定系数r2为0.88，平均平方误差mse为1872，均优于单一模型，这证明了xgr-stacking融合模型在预测房价问题上更具优势。
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research on stock housing price prediction based on xgr-stacking model
abstract：china has now entered an era of stock housing. with the transaction prices of stock housing in cities like shanghai, nanjing, and hangzhou being hidden, market information asymmetry has increased, complicating and making transactions more difficult. additionally, the "old-for-new" policy in the real estate industry encourages residents to sell stock housing and purchase new homes. therefore, accurately predicting stock housing prices is particularly important. currently, machine learning is widely used for housing price prediction, but most models used are single models, which tend to suffer from overfitting and limited expression capabilities. to overcome these shortcomings, this study uses transaction data of stock housing in nanjing. through correlation analysis, the 12 factors most influencing transaction prices were selected to establish three single models: xgboost, random forest, and gbdt, along with the xgr-stacking ensemble model. using mean squared error (mse) and the coefficient of determination (r2) as evaluation criteria, results show that the xgr-stacking model has an r2 of 0.88 and an mse of 1872, both superior to the single models. this demonstrates that the xgr-stacking ensemble model has significant advantages in housing price prediction.
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基于xgr-stacking模型的存量住房价格预测研究
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