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.
Highlights
Such recommender systems are widely deployed in many consumer domains, such as online shopping, our research focuses on real estate recommendations
The critical aspect to notice in the price prediction model is that the data used for this analysis is the “offered set of prices” by the real estate portal Zameen.com
Prediction models were created, and results were visualised by price increase, decrease or stagnancy in multiple sectors of Islamabad city to better assist people planning future land asset purchases
Summary
Driven by advertising technologies and goals to produce targeted ads, the personalisation and customisation of websites and services have become the new norm in our society. Another aspect growing in popularity is having the location information of a user to make recommendations Such recommender systems are widely deployed in many consumer domains, such as online shopping, our research focuses on real estate recommendations. Real estate recommendation is often about the location of a property item, so we have incorporated online map interactions as a tool to understand a user’s interests. This paper presents four principle recommendation approaches for effectively identifying property items in our real estate portal. The reason for selecting the first two approaches is based on the fact that the features of a real estate database closely resemble a movie database Both content-based filtering and collaborative filtering have proven to provide precise recommendations to users [1].
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