Hi, my name is Preston.

How should a home acquisition operator jointly choose an offer and a resale-price path when seller acceptance, buyer demand, carrying costs, and terminal value are uncertain?

Offer-to-Exit: Pricing a Home Under Uncertainty

Offer-to-Exit is my featured GitHub project on the property-level economics of a housing market maker. A higher acquisition offer raises the probability of seller acceptance but compresses the margin conditional on purchase. A higher resale price raises proceeds conditional on sale but lowers the weekly sale hazard and increases expected carrying costs. Because the value of buying a home depends on the best feasible exit policy, the acquisition and resale decisions are solved jointly over a 17-week horizon.

The real-data study uses 2,657,072 official sale records from Hillsborough County, which contains Tampa, and Orange County, which contains Orlando. A repeat-sale valuation model is trained before 2022, calibrated in 2022 and 2023, and tested on Tampa sales from 2024 through August 2026 before being carried to Orlando without refitting. Its mean absolute error is $76,503 against $78,268 for a rolled-forward prior-sale benchmark in Tampa, and $129,039 against $147,902 in Orlando. The repository also links 5,060 named iBuyer acquisition spells, including 3,395 for Opendoor. Its duration model ranks future exits modestly but is poorly calibrated, so the project recommends abstention or recalibration rather than deployment.

Deeds do not reveal seller acceptance under offers never made or buyer demand under prices never posted. A separate controlled experiment therefore varies offer-to-value ratios and list-price premia, fits a binary-choice acceptance model and a right-censored weekly sale hazard, and feeds those fitted responses into the dynamic program. The acquisition objective maximizes expected contribution value net of a configurable penalty on positive losses in the worst decile of stress outcomes. The experiment demonstrates model recovery and decision mechanics under known counterfactuals. It does not estimate real-market price responses or profit lift.

View the GitHub repository and evidence

The Heterogeneous Impact of a Homicide on Nearby Property Values Across Demographic Characteristics

Violent crime is a neighborhood disamenity, but the raw correlation between crime and house prices does not identify capitalization because crime moves with persistent neighborhood attributes that also determine prices. The paper treats the precise timing and location of a homicide as a plausibly exogenous shock to perceived safety, conditional on local time effects and observed housing and incident characteristics. The economic object is the market's capitalization of newly revealed risk into transaction prices, including how quickly that response decays with distance.

Identification comes from a spatial difference-in-differences design using 73,082 arm's-length single-family sales in Miami-Dade County from 2010 to 2018. The analysis compares the change in log sale prices within 0.1 miles of a homicide with contemporaneous changes in the 0.1-to-0.2 and 0.2-to-0.3 mile rings. The event window spans two years before and two years after the incident. Census-block-by-year fixed effects absorb common neighborhood-year shocks, while detailed property and homicide controls address observable composition. The identifying assumption is local parallel trends: absent the homicide, transaction prices in the inner and outer rings would have evolved similarly. A triple-difference extension estimates how this local response varies with victim and offender characteristics.

The paper tests the credibility of that counterfactual rather than treating it as automatic. In the pre-period, price and housing-characteristic differences across rings are small and statistically insignificant after controls, and graphical trends are broadly parallel. Assigning false event dates one and two years earlier produces null effects. Restricting the sample to sales inside the 0.3-mile event area and replacing block-by-year fixed effects with homicide-area-by-year fixed effects yields a similar estimate. The preferred result is a 4.8% decline within 0.1 miles, with no detectable decline in the next two rings. The estimand is the effect on observed transaction prices, not the value of every home; selection into sale and external validity beyond Miami-Dade remain important limits.

Read the paper (PDF)