08 Aug Real estate solution to link home owners to potential tenants
“Buyers decide in the first eight seconds of seeing a home if they’re interested in buying it. Having screens where it provides a vivid picture about the property in the application helped to increase sales”
– Barbara Corcoran
In recent years, the African real estate market has blossomed and was ranked to be the most attractive region worldwide for investments in real estate. Said that, people are actively looking for better investments and also interestingly in search for a way in which will help them take their decision wisely and quickly.
Over the years real estate investors had to only rely on newspapers or any real estate brokers in order to reach their potential client market. This system would help them manage the real-estate sales effortlessly and quickly.
This application acts as a premiere platform which allows buyers and investors who are looking to buy land or property to directly get in touch with the seller. The idea behind developing this app was to have an application that allows users to view the list of available rental and sale of real estates and properties. One of the key concerns of sellers is inability to estimate prices for the properties thus losing out huge potential of clients by not pricing according to the on-going housing market rates in the economy. Hence, therefore one of the core features of the application is the machine learning model used to predict the best price for that property using certain factors. In this way the seller is aware of what the market price is and how much he/she expects.
We strive to deliver an application that is efficient and robust. But in order to maintain this effectiveness and robustness we need to ensure that this application can handle millions of requests and data transfers which comprises of high quality resolution images. And the functionality needs more focus as the end users are not tech geeks but ordinary people with an average tech knowledge of using a smartphone. As the user base of the application is growing day by day we had to ensure that our application handles all this requests, traffic and is having an architecture which supports scalability. Therefore our application and API’s had to be optimized to its best to keep up with the performance required. We also had to make sure the application is a dynamic platform with automatic updates so that users are always up to date and enables to get the best service of it.
Millions of people access the application at the same time and also make API calls at the same time which will require high processor usage and computing power.We had to get the best servers in the market and cater it to make sure to run our application in an optimum manner as it also needs to process the in house machine-learning framework. After analyzing the competitors of the real estate market providing similar solutions, it was identified that one common challenge users faced was the inability to determine housing prices and thus some sellers not being able to sell the properties faster. Therefore, we had to build an in-house machine-learning framework to predict prices and also run the process vertically scaling up the workloads.
An application that can help millions of users to find a property that matches their budget and a place to live as they desire. And also a profitable platform for those who are selling their property. The application allows the available properties to be displayed as a pin in the Google maps for ease of location. All this makes it possible for the interested users to narrow down their search for properties.
One of the interesting features of this app is Augmented Reality takes the users through a virtual tour of the properties over the iPhone screen (exclusively made available for IOS only) With the integration of the Machine learning the application also allows users to predict the value of the property and then help users decide on the price at which they can sell the property. Also as the user are registered with it is easier for the seller to reach the customer and also target them.