Home / Case Studies / Hong Kong Real Estate Group
/ Case Study · Real Estate
A Web App to View the Latest Property Listings. For Rent or Sale, in One Search.
/ The engagement
A.Net Core App for Property Listings and Rentals.
A Hong Kong group of real estate agents and tech entrepreneurs wanted to simplify property buying and renting. Brainvire built a.Net Core web app that lists properties for sale or rent, adjusts pricing with a machine learning model, takes secure payment through Stripe, answers tenants with a Chatter Bot, and filters thousands of listings by location, price, and criteria.
/ About the client
The client belongs to a group of real estate agents and tech entrepreneurs based in Hong Kong. The group aims to tailor the needs of both real estate professionals and home seekers by simplifying the property buying process in Hong Kong.
The client approached Brainvire to craft a.Net Core web app that showcases properties for sale or rent. Customers looking for a local or global home find the right property by scrolling the list, an automated process keeps listings up to date, and a tenant management system tracks payments, leases, and requests across all devices.
/ The approach
Design, architecture, and features, shipped as one program.
Set by the data
The client wanted pricing tuned to each property, so the Business Intelligence team used a machine learning algorithm to adjust pricing by location and property characteristics.
Secure checkout
Rental and sale transactions had to be safe, so the team integrated Stripe through its API for quick, secure payment.
Agents and tenants connect
A communication gap sat between agents and tenants, so the team installed a Chatter Bot, preprocessed the dataset, and trained an ML model to keep the conversation going.
The right home, fast
Customers needed to find a property among thousands, so the team added a filter search by location, price, and other criteria to save time and effort.
/ What stood in the way
Four problems, solved without a maintenance window.
Select a challenge to see how it shaped the build.
Automated pricing
The client wanted a central place to track data in a reporting format and adjust pricing to each listing. Our Business Intelligence team used a machine learning algorithm to unearth insights and automatically adjust pricing by location and property characteristics.
Integrating Stripe payments
The main aim of the platform was a secure checkout. To let users pay for rental or sale transactions safely and quickly, the team integrated the Stripe gateway using the right Stripe API.
Implementing a chatbot
Agents and brokers could not stay in frequent contact with tenants, and the communication gap led to several limitations. We installed Chatter Bot, a Python library, preprocessed the dataset, and trained the bot to improve its performance.
A filter search option
The client wanted an app where customers easily find their desired properties. The app lists properties for rent or sale with a filter search that helps customers find the right one among thousands, saving time and effort.
/ Product screens
The delivered experience.
The interface people actually use.
/01Listings
/02Home
/03Property List
/04Property View
/05Dashboard
/06Search
/07Mobile
/08Reviews
/09Detail
/10Map/ The results
What changed, in numbers.
/ Next step
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