Using geographic plotting and data science tools easily accessible via Jupyter’s Python interface, we’ll discover realtor farms, and assess the characteristics of sales vs listing price. Real estate transactions tend to be geographically sparse and temporally rare. There is often both a listing and a selling agent in the representing a given property. The sales price is determined by a number of factor. While there has been considerable interest in building pricing models relying on physical parameters, there has been little work done in assessing the contribution of the realtor, herself. The discovery of a ‘farm’ uses cluster identification methods. These farms can then be analyzed for imputed listing prices and the sales price, both of which are negotiated.
The problem: Most real estate analytics deal only with property description and location. Markets can swing quickly from buyer’s to seller’s advantage, so timing and days on market is important. Examples of discovered geographic affinity will be presented. Agent effects are not well understood and can be a significant factor in determining the listing and actual selling price.
By examining over/under-performance of sales price to listing price the relative negotiation skills of listing and selling agents. A common concern is the effect of dual agency on pricing. Finally, an exploration of whether “Farms” (created by listing agents) have real consequences on neighborhood pricing.
Home ownership is the largest transaction, yet there is little data available concerning the contribution and effectiveness of realtors.
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