ABSTRACT
Big Data has transformed the workings of real estate firms by improving the efficiency, cutting costs and by enhancing decision making. It helps them to become more agile for improved customer satisfaction and experiences. In the past, real estate businesses had to follow traditional methods by following past trends and professional expertise to make major decisions. Big Data has become much easier to access accurate real data, make conclusions and to even predict future prices of properties. This research uses machine learning algorithms for the appraisal of property prices in New York City. The methods are applied to the data sample of about 80,000 properties, which have sufficient information about each property and its demographic aspects. By further analysis and modelling, it is observed that model with Feature Engineering has performed much better that the model in which Random Forest was implemented. The conclusions drawn from the empirical study would be beneficial for real estate agents and people who are looking forward to invest in New York properties and understand the variation of property prices of New York in the post covid era in comparison to the pre covid era. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.