A Map-Based Recommendation System and House Price Prediction Model for Real Estate

Abstract

In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase.