Abstract:
In 2015, the worth of global real estate was $217 trillion, which approximately makes 2.7 times
the global GDP. This also accounts for roughly 60% of all conventional global resources,
making real estate as one of the key factors behind any country’s economic growth and
stability. Since location is termed important when it comes to real estate and consequent
decision-making, digital maps have become an exceptional source for real estate purchases,
planning and development. Personalisation can assist real estate users to make judgments by
identifying any person’s desires and inclinations, which can then be recorded or captured as a
user performs interactions with a digital map. This information can then be used by a
personalised real estate map to suggest properties on the internet, assisting homeowners and
providing useful real estate analytics. For users of a real estate platform, we have created a
recommendation engine based on content, collaboration and location providing users with
useful recommendations along with a house price prediction model, which employs the
technique of multiple linear regression and neural networks. Our prediction model classified
increasing, decreasing and stagnancy of pricing trends in different areas of Islamabad city. The
results show 79% precision on recommendation and an accuracy of 80% on price prediction.
This approach can be useful for Pakistan's real estate industry, with the primary goal of
changing current management practises by employing GIS and data analytics as enduring
solutions.
