A typical home seller in the U.S. in 2017 was 56 years old. They had lived in their house for the last ten years and had a median income of 107 thousand dollars. While the median age of a first time home buyer was 33, and 47 for a repeat buyer, with respectively 80 thousand dollars and 106 thousand dollars of income. The standard home bought was 1,900 square feet in area, was constructed in 1993, and had three bedrooms and two bathrooms. And on average, 88% took a loan to finance their purchase. Needless to say that, for such a substantial debt contracted, the actual price negotiated has to be as close as possible to the fair market value.
We have used TADA with a dataset describing 409 transactions. It is a regression use case. The following information is included for each transaction:
The goal is to see whether TADA can estimate the home selling price accurately. The price estimation has been mostly dependent on the realtor’s evaluations. However, it is precious for a home seller and a realtor to get a quick, independent, objective estimate in a few clicks.
It raises the following real estate question:
Can the price of a home be evaluated automatically with accuracy?
The TADA predictive models are accurate in home prediction with a MAPE of 14% and an R2 of 77%.
In general, a real estate agent participated in 89% of home sales. There is a benefit to being a recent seller as they typically sold their homes for 99% of the listing price. 38% of all sellers described decreasing the asking price at least once. The average home sold was on the market for three weeks. These figures show the pertinence of choosing the right selling price right away because it eases both price negotiations and sale delays.
TADA has selected the following four criteria out of the eight available in the dataset to make its home price evaluations:
In 2018, 8% of homes were sold by their owners. Owners selling their home by themselves say that getting the right price and selling within the planned length of time was among the most challenging tasks. Finding the right selling price and optimizing it against the time required for selling are challenges. The live predict feature of TADA can help model this.
In two days, home sellers, real estate agents, and homebuyers can obtain: