The rise of social networking platforms provides opportunities to examine the relationship between public emotion and housing price. This study investigates frequency and places of visits, population sentiment, and housing price using 8.7 million tweets retrieved from Manhattan, New York City in 2019. We implemented kernel density estimation, Getis-Ord G_i hot spot analysis, and spatial lagged hedonic pricing models to identify the location variation of sentiment levels. The results show: (1) the spatial clustering of tweets frequency was highly related to land use types in places such as parks, financial districts, and train stations; (2) high sentiment levels coincided with high frequency clusters and higher positive sentiment is associated with higher housing price; and (3) sentiment level was significantly associated with housing price and building structure, amenities, and proximity to landmarks all had significant influences on housing price. The study indicates that a population with higher concentration of happiness correlates to higher property value and provides an innovative perspective to understand public sentiment in relation to housing price using social media data, supplemented by housing transaction data. We demonstrate a feasible framework for researchers and stakeholders to utilize in future urban and spatial geographical research.

社交网络平台的兴起为检验公众情绪与房价之间的关系提供了机会。这项研究使用2019年从纽约市曼哈顿检索到的870万条推文，研究了探访的频率和地点，人口情绪和住房价格。我们实施了内核密度估计，即Getis-Ord G _i热点分析和空间滞后享乐定价模型来识别情绪水平的位置变化。结果表明：（1）tweets频率的空间聚类与公园，金融区和火车站等地方的土地利用类型高度相关；（2）高情绪水平与高频集群同时发生，较高的积极情绪与较高的房价相关联；（3）情绪水平与房价，建筑结构，便利设施以及地标的接近度均显着相关，均对房价产生重大影响。该研究表明，幸福感高度集中的人群与较高的财产价值相关联，并提供了创新的视角，可以使用社交媒体数据来了解与房价相关的公众情绪，补充住房交易数据。我们为研究人员和利益相关者展示了一个可行的框架，可用于未来的城市和空间地理研究。