Given basic parameters about a dwelling such as location, square footage, bath count, etc., our model is able to predict the likely quality of the materials used to build that dwelling. However, no prediction will be more accurate than an actual inspection or detailed analysis with first-hand knowledge.
When you run your first search, the system will default to the “DC Q-Score" as a quality rating. Using the Q-Score will tell our model to calculate a replacement cost estimate based on our predictive quality score as opposed to your first-hand knowledge of the property. While this isn’t meant for every search you run, we recommend using the DC Q-Score for the following use-cases:
Get a replacement cost estimate in seconds for prospective leads and clients without the need to rely on their subjective opinion of the dwelling.
Our Q-Score enables a variety of research needs, from single users trying to better understand a new target market, to data analysts leveraging our API to analyze a large sample of properties to better understand the impact of regional events (natural disasters, supply shortages, etc.).
We understand our clients have a need for multiple data sources to establish the most accurate replacement cost possible; the Q-Score allows you to quickly get a second opinion on estimates from other data sources or calculation methods.
When detailed information regarding the property is unavailable via public sources.