LotLens

LotLens finds properties for auction, ranks them by planning approval rates, and gives you local prices, rental yields, and 3D floor plans so you bid smarter and renovate with confidence.

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Inspiration

We built LotLens to give every bidder the intelligence they need before the hammer falls

What it does

LotLens is a web app that provides UK landowners with a file containing all the important information that they will need to make the right decision. All you need to do is enter a location, price range and amount of bedrooms and LotLens will handle the rest. LotLens will scrape live and upcoming auction listings and will give a full breakdown of: images, floorplans, reccomended bid, monthly rent and local planning application analysis among other things. What it does is provide 3 listings which match the best to the recquirements and compare the price to local properties and even generate a 3D visualisation from the floor plan.

How we built it

We built a React and TypeScript frontend and a Python Flask backend. Users enter location, price range, and bedroom count and the frontend then talks to our Flask API. We pull property and land data from the government’s open land property dataset and rent statistics (by area and bedroom type) from the ONS rent statistics dataset. For each search we scrape live and upcoming auction listings from Auction House, rank them using planning data from the Ibex API and postcodes.io for UK postcode lookups, then return the top matches with images and floor plans. Floor plan images are sent to the OpenAI API for analysis so that we can render 3D simulations. The resulting walls and room labels feed our 3D viewer built with Three.js and React Three Fiber. We use the same OpenAI API to suggest extra bedrooms and bathrooms and to power the planning breakdown. The app generates a single PDF report via jsPDF with recommended bid, monthly rent, local planning analysis, and the 3D visualisation, so users get one file with everything they need to complete the user experience.

Challenges we ran into

Our 3D render was the most challenging part of our project. Floor plans each differ from each other for every lot and there always is an exception which presents itself. Covering exceptions and to make the code understand how to coordinate the different exceptions and to deal with different floorplans and different styled floorplans was our biggest challenge by far. Other challenges we ran into were making our idea unique and niche and focusing on a certain part, coming to the final idea that we have has taken up a large amount of time.

Accomplishments that we're proud of

We were able to achieve our end goal of creating a platform and a solution that we're proud of to landowners. We are also proud of making this to industry standard by checking with people working within this industry to help guide us to make it useful.

What we learned

We learned that property data is scattered amongst different sources, databases and wesbites.

What's next for LotLens

Our next step would be to make a more realistic version of the 3D rendering. Additionally, we could expand our databse to not only focus on UK properties but also other European countries or cities. Build more on the extra add on for bathroom and bedroom to make it more engaging for the user to click and eventually for us to earn more income from each addition.

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