A Peek Into My Offer Pricing Process Pt.1
Jason Cochard for Land Academy
To assist me in offer pricing, I am using a combination of sold comps and on-market comps. There is compelling reason to go with on-market only, namely that bulk sales and non-arms-length transactions will introduce inaccuracies to the prices of sold comps (to say nothing of typos). With my limited experience, I currently use sold comps at the end of the process, as a sort of sanity check.
Working backwards, for sold comps, I get the info from the downloaded dataset. Keeping in mind that it’s an incomplete dataset where any given price may be a portion of a bulk sale even if I can’t see the rest of the sales. But with any luck, we can deduce the bulk sale. In the CSVs that I download, there’s a field for prices for Last Owner Transfer and one for Last Market Sale. Usually owner transfers are skewed low because they’re not arms length transactions, so if there’s a number in both fields, I go with last market sale. This prevents errors due to family members selling a property to each other for a fake or low price.
To attempt to mitigate the errors arising from multi-parcel bulk sales skewing last market sale price high over several properties, I sort the dataset by the last market sale numbers, and see which properties have exactly the same price. When one deed conveys multiple properties, chances are the county employee entering the data has not divided the purchase price by the number of parcels conveyed. Thus, multiple parcels conveyed for exactly the same price.
From there, to the extent the CSV is complete, I can look at columns like seller/buyer, etc, to sleuth a little bit to see if I can get an idea of how many properties were sold in that same sale (knowing that it might be more parcels in any given bulk sale than are reflected in the dataset I downloaded). To find out quickly, if I suspect there’s a bulk sale impacting a price, I can look up a recorded deed to see how many properties are on the same deed, and I can then divide to get a per-parcel price in a given bulk deal. From there, the numbers for the parcels in that bulk deal can be revised to a more sane level. This is borderline “too much work” but it does give you some peace of mind — our offers do cost us money.
For on-market comps, I ignore really off-base outliers right off the bat, to protect the averages, and use days on market to change the weighting for high prices (normalized to price per acre, but using a search of like-kind acreage range so it doesn’t skew to a different range’s per acre pricing). In doing the weighting, you could see some properties that have high pricing and really long DOM. Based on the prices of the high DOM properties, I note that price and change the weighting for newly posted like-kind listings in the same price range — ie, posted recently, yet priced in a way that makes it likely to become a similar high DOM by the time it sells. When creating my average price per acre that will form the basis of my offer, this incrementally-reduced price is what I will use for all properties with the highest DOM.
What I’m doing here is trying to achieve something akin to the relative strength index or bollinger bands that analyze for overbought/oversold stock market pricing. In an overbought market, you know that the price is too high and will correct downward — to me, this is what high DOM is indicative of. Thus, either the DOM will continue to rise, or the seller will revise the price downward to cap the DOM. In an oversold market, you know the price is too low and will correct upward, but rarely would we ever see that, or care (or, perhaps we can see that, and will care — stay tuned). Vacant land is a much different game than the stock market, but it still helps to devise a way of systematically calling BS on prices.
The last thing I do — and I’ll go into detail next time — is to separate the on-market comps into the investor tier and the realtor tier. Generally, but not all the time, realtors are priced higher than investors. Thus, we can begin to get an idea of the investor range for offers, and the realtor tier for sales.
Next time, I’ll continue the discussion of pricing, expanding to sales pricing, which of course are linked to and enabled by offer pricing. And perhaps we can identify oversold pricing and employ a strategy to correct it to a place where the market will respond.