Real Estate · Scenario 11

Residential Brokerage

List too high and the home goes stale; too low and money is left on the table. We price listings with a regression on real comparable sales, not a hopeful round number.

Method · Regression valuation

The situation

A mispriced listing is the most expensive mistake in real estate. Too ambitious and it sits, accrues ‘days on market’ stigma, then sells below where a correct price would have landed. Too cautious and the seller quietly loses thousands. Yet many list prices still come from eyeballing three comps and rounding.

Agents lack a defensible, data-driven valuation — so pricing conversations with sellers become negotiations of opinion, and the listing strategy has no model underneath it.

18%
listings priced >5% off
47 days
avg time on market
3 comps
typical basis for price
Opinion
how price is set

Where we dig for the truth

We build a hedonic pricing model from years of local sales — letting floor area, beds, location, condition and timing each carry their true weight.

Local sold comparablesBeds / baths / sq ftLot & location featuresCondition & renovationsDays-on-market historySeasonality of sales
Sale price vs floor area, with the model lineEach dot a recent comparable sale; the gold line is the fitted price$0k$250k$500k$750k$1000k0500100015002000Floor area (sq ft)Sale price (CA$000s)

The model prices a home from dozens of comparable sales at once, not three — and quantifies exactly what an extra bedroom or a renovated kitchen is worth.

Our approach — Hedonic Pricing Regression

A hedonic regression estimates the market value of each attribute — floor area, bedrooms, location, condition — from years of sold comps, then prices a new listing within a tight, defensible range. Agents walk into the listing appointment with evidence, not a guess.

Pricing to the model’s range avoids both the stale-listing trap and the leave-money-behind trap. Marketing then concentrates on the homes and buyer segments the data says will move fastest.

From three comps to a valuation model1Gather compsAssemble years of localsold listings and theirattributes.2Fit the modelRegress price on size,location, condition andtiming.3Price the rangeSet a defensiblelist-price band foreach new listing.4Position to sellAim marketing at thebuyers the model saysrespond fastest.
Days on market by pricing accuracyHow long homes take to sell0d23d46d69d92d78d41dPriced >5% high44d29dWithin 5%26d24dPriced lowBeforeAfter

Model-priced listings spend far less time on market — and because they don’t go stale, they sell closer to full value.

What changes

Same homes, priced with evidence. Representative for a small brokerage or top-producing agent.

Representative 90-day movementAvg. days on market4729▼ -38%Sale-to-list ratio96.5%99.2%▲ +2.7 ptsListings won at pitch52%71%▲ +19 ptsAnnual GCI$310k$430k▲ +39%
Where the extra commission comes from+$120kannual GCIFaster, fuller-price sales42%More listings won38%Fewer price cuts20%
Why this is not "social media management"
We didn't just make prettier listing brochures. We gave the agent a valuation model that wins the listing and sells it for more — the analytical backbone behind the marketing, which is where deals are actually won or lost.

Frequently asked questions

How do you price a home listing accurately?
We build a hedonic pricing model from years of local sold comparables, letting size, location, condition and timing each carry their true weight. Listings priced to the model sell faster and closer to full value, avoiding the stale-listing trap.
What is hedonic pricing regression?
It is a statistical method that estimates how much each feature of a property — an extra bedroom, a renovated kitchen, a better location — adds to its sale price, using many past sales rather than three hand-picked comps.
Isn't this just listing photos and marketing?
Photos help, but the listing is won or lost on price. We give the agent a defensible valuation model that wins the listing and sells it for more. Book a marketing audit.

Want this run on your numbers?

Send your local sold data and we’ll build the pricing model behind your next listing.