AI Tool Detects Home Issues Before Inspectors

The integration of artificial intelligence in various industries has opened doors to innovative solutions, and real estate is no exception. Among the most notable advancements is AI staging, which transforms images of empty rooms into inviting spaces filled with furniture, artwork, and even, as seen in a viral rental listing from a Washington, D.C. suburb, an unsettling creature emerging from a bathroom mirror.

However, a new AI tool from Colorado’s Alpine Building Performance aims to offer a more constructive application. Launched in March, the decade-old home inspection company introduced a free tool named Alpine Intelligence. This innovative application forecasts potential issues in homes. “Our AI inspection forecaster is designed to shift agents from a reactive to a proactive approach,” explains Andrew Sams, the founder and owner, who collaborated with partner Mason Minor to create this ChatGPT-powered tool.

The concept for Alpine Intelligence emerged long before the technology that supports it, driven by the chaotic housing boom during the pandemic. Sams recounts, “People were entering contracts almost without seeing the property. They would spend just five minutes inside and make offers significantly above the asking price.” He noted that agents often reached out for assistance due to the urgency of putting in offers without knowing the condition of the homes, leading to an overwhelmed workload for home inspectors.

Even in today’s slower market, buyers often have limited time after an inspection to decide whether to proceed, withdraw, or request concessions from the seller. Understanding the costs associated with issues—like replacing outdated aluminum wiring—can give buyers an edge. “The frenzy of the pandemic made me realize that many inspection issues are typical of a home’s age and location; there’s a predictive analytics component at play,” Sams states. “We essentially developed a specialized knowledge database.”

While anyone can utilize Alpine Intelligence by uploading a home’s MLS data and relevant information such as permit histories and disclosure statements, the platform excels at analyzing properties along the Front Range, particularly those built prior to 1930. “These classic homes in neighborhoods like Congress Park, Cheesman Park, and Highland often feature brick masonry foundations, which are susceptible to mortar deterioration,” explains Sams. “Additionally, homes built before 1940 have an 87 percent likelihood of containing lead-based paint.”

Nevertheless, this also indicates a 13 percent chance that a home may not have lead paint, reaffirming the need for traditional inspections. “The tool is certainly not intended to replace inspectors but to aid agents in managing expectations,” Sams clarifies. “If a buyer encounters an issue common to 1950s homes in City Park and decides to walk away from that deal, they may face the same issues in their next prospective home.” In this scenario, leveraging AI knowledge could prove immensely beneficial.

Read More: Buying or Selling a Home in Denver This Year? Here Are 5 Questions You Should Ask.