Resumen
The accessibility of spatial big data help real estate investors to make better judgement calls and earn additional profit. Since location is considered necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can support in making judgments by identifying user requirements and inclinations, which a user interacts with digital map, it records all the user?s activities. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. By monitoring user interactions through an online real estate portal, the framework provided in this article 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. Out of 5 recommendations produced, users were interested in at least 3. A separate house price prediction model was also developed base on neural networks and classical regression technique. This model implemented to assist users in making an informed decision regarding prospects of real estate purchase.