Our inspiration came from personal experiences of buying and picking out the perfect home. While services like Zillow and Realtor already provide an online real estate shopping experience. There's no web application available that can tell you, is this house a good deal? We believe the real estate buying process doesn't have to be so difficult. And it doesn't need a realtor either (whose job is to sell you a house). By automating the research, our customers can save money and feel confident in their purchase.

What it does

Our application asks the user to input a real estate listing and outputs a report card of whether the house is priced well or not based on the provided stats It works in the background with a public database of real estate sales and uses that data to determine if the input listing is above or below market value. To find whether a listing is above or below market value, we compare the input listing to the most recently sold listings within a mile that are similar to that house, based on square footage, age, and the number of bed/bath. A large part of this project was managing the data, sorting it by category, and making the search process easier.

How we built it

We did the planning and Prototyping with notion and Figma, Bootstrap, Node. js, EJS, and Express.js for building our website, Heroku for hosting our application, and we used python and SQLite for managing our data.

We created property classes and handled all API operations in one class

Challenges we ran into

None of us have built an application like this before so we were learning at every step. A big challenge from the initial process was determining what our application could offer within the given time frame. On top of evaluating the housing deal, we wanted the user to be able to find similar listings to add to their search. We had to answer questions like, what does our program offer that makes it stand out from our competitors? Another challenge that hindered our process was data scraping. While it would have been extremely useful to pull data from sites like Zillow, we were unable to gain access to these APIs. Because of this, the application that we currently have is only able to search through houses that are within the database we pull from.

Accomplishments that we're proud of

We're extremely proud of how our website looks and our ability to work with a large batch of data to output a result.

What we learned

We learned how to evaluate a house listing price and what goes behind being able to get an appraisal.

What's next for klove - rating real estate deals

We hope to expand our application to include user preference. Additionally we want to be able to expand our database so that we could increase the range of houses that we compare prices to.

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