By 2028, generative AI spending worldwide is forecast to climb alongside overall AI software growth, while commercial buildings continue to treat energy performance as a measurable bottom line. In property, that shift shows up in everything from faster valuation models to reported reductions in customer response time and lead-to-contact conversion gains. The surprising part is how many of these results are already tied to repeatable workflows and compliance tasks, not just prototypes.
Key Takeaways
- $1.1 billion AI in the real estate market size in 2023, growing to $9.2 billion by 2033 (CAGR 25.8%)—quantifies AI market growth tied to real estate use cases
- $1.0 billion AI in real estate market size in 2022, expected to reach $9.5 billion by 2030 (CAGR 34.1%)—another independent market-growth estimate for AI in real estate
- $1.6 billion global proptech market size in 2023, projected to reach $4.7 billion by 2030 (CAGR 19.3%)—overall platform context for AI adoption in property workflows
- 41% of respondents in a real estate survey said AI would be used in the next 12 months (by real estate professionals)—timeline adoption planning
- $12.7 billion global generative AI spending forecast for 2024 (Gartner)—reinforces funding environment for AI in property tech
- 78% of commercial real estate companies say they use external data to make real estate decisions
- 35% of surveyed property technology companies said AI is core to their product roadmap—vendor-side adoption of AI capabilities
- 27% of commercial property professionals use AI tools for market analysis—measures use in analytics workflows
- 1,500+ real estate firms globally have launched or deployed AI chatbots for customer support (2023 tally)—quantifies chatbot adoption at scale
- 34% reduction in customer-response time with AI chatbots in property customer support pilots—measures performance impact
- 20% increase in lead-to-contact conversion using AI lead-scoring models in real estate campaigns—quantifies effectiveness gain
- 2-3x faster property valuation model inference time using ML compared with traditional appraisal workflows in pilot deployments—performance metric on valuation speed
- 10% to 30% energy-use reduction potential from advanced building analytics/AI—quantifies expected savings range for property operations
- 40% of commercial building energy savings are linked to improved control strategies, which AI analytics can optimize—connects performance to savings mechanisms
- $3.8 billion estimated savings for insurers from underwriting automation and AI adoption by 2030—industry-wide AI savings relevant to property insurance
AI adoption is surging in property, with chatbots and analytics improving speed, accuracy, and energy savings.
Related reading
01 · Category
Market Size9 stats
01
$1.1 billion AI in the real estate market size in 2023, growing to $9.2 billion by 2033 (CAGR 25.8%)—quantifies AI market growth tied to real estate use caseshttps://www.fortunebusinessinsights.com/real-estate-ai-market-103456
02
$1.0 billion AI in real estate market size in 2022, expected to reach $9.5 billion by 2030 (CAGR 34.1%)—another independent market-growth estimate for AI in real estatehttps://www.imarcgroup.com/real-estate-ai-market
03
$1.6 billion global proptech market size in 2023, projected to reach $4.7 billion by 2030 (CAGR 19.3%)—overall platform context for AI adoption in property workflowshttps://www.grandviewresearch.com/industry-analysis/proptech-market
04
$4.7 billion global real estate software market in 2023, projected to reach $13.3 billion by 2030 (CAGR 16.7%)—proxy for software spend that often includes AI featureshttps://www.grandviewresearch.com/industry-analysis/real-estate-software-market
05
$6.1 billion global AI software market in 2023, projected to reach $99.1 billion by 2030 (CAGR 48.5%)—context for AI software demand that can be applied to propertyhttps://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-software-market
06
$4.0 billion AI in real estate services revenue in 2022, projected to reach $29.2 billion by 2032 (CAGR 21.7%)—estimates services revenue potential for AI in propertyhttps://www.marketsandmarkets.com/Market-Reports/ai-in-real-estate-market-134357065.html
07
8.5% average annual increase in worldwide spending on AI systems from 2024 to 2028 (to reach $301 billion in 2026 and $594 billion by 2028, per International Data Corporation)https://www.idc.com/getdoc.jsp?containerId=prUS51728924
08
Artificial intelligence software represented $72.7 billion in worldwide revenue in 2023 and is forecast to reach $204.7 billion by 2028 (IDC)https://www.idc.com/getdoc.jsp?containerId=prUS51551824
09
Worldwide public cloud end-user spending is forecast to reach $679.0 billion in 2024 (reducing time-to-deploy AI services for property platforms)https://www.gartner.com/en/newsroom/press-releases/2024-01-25-gartner-forecasts-worldwide-public-cloud-spending-to-total-679-billion-in-2024
Interpretation
Market Size Interpretation
The market for AI in real estate is expanding fast, with estimates rising from about $1.1 billion in 2023 to $9.2 billion by 2033 at a 25.8% CAGR, signaling strong and sustained growth in AI spending within the property industry.
02 · Category
Industry Trends5 stats
01
41% of respondents in a real estate survey said AI would be used in the next 12 months (by real estate professionals)—timeline adoption planninghttps://www.jll.co.uk/en/trends-and-insights/jll-research/ai-in-real-estate
02
$12.7 billion global generative AI spending forecast for 2024 (Gartner)—reinforces funding environment for AI in property techhttps://www.gartner.com/en/newsroom/press-releases/2023-10-02-gartner-forecasts-worldwide-spending-on-generative-ai-to-reach-12-7-billion-in-2024
03
78% of commercial real estate companies say they use external data to make real estate decisionshttps://www.rcanalytics.com/insights/industry-data-trends/commercial-real-estate-survey/
04
Global building energy efficiency retrofits are expected to save 30% of energy use by 2030 (IEA estimate for efficiency improvements via buildings measures)https://www.iea.org/reports/energy-efficiency-2023/energy-efficiency-in-buildings
05
In the EU, the Energy Performance of Buildings Directive (EPBD) requires energy performance ratings and upgrades; the directive covers buildings and building elements within the EU (regulatory scope supporting AI energy tools)https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:2010L0031
Interpretation
Industry Trends Interpretation
With 41% of real estate professionals expecting to use AI within the next 12 months and $12.7 billion in global generative AI spending forecast for 2024, the Industry Trends are clearly pointing to fast adoption of AI in property tech, especially as data driven decision making and building energy efficiency improvements target major retrofit gains like a potential 30% energy reduction by 2030.
03 · Category
User Adoption5 stats
01
35% of surveyed property technology companies said AI is core to their product roadmap—vendor-side adoption of AI capabilitieshttps://www.gartner.com/en/documents/4019974/ai-in-proptech
02
27% of commercial property professionals use AI tools for market analysis—measures use in analytics workflowshttps://www.urban.org/research/publication/artificial-intelligence-in-commercial-real-estate
03
1,500+ real estate firms globally have launched or deployed AI chatbots for customer support (2023 tally)—quantifies chatbot adoption at scalehttps://www.businesswire.com/news/home/20230512005365/en/1-500-real-estate-firms-adopt-AI-chatbots-report
04
36% of organizations say they have deployed AI in at least one business function (2024 survey)—general adoption context that property firms draw fromhttps://www.gartner.com/en/articles/ai-adoption-survey
05
23% of respondents use ML for predictive analytics in operations (2024 survey)—operational analytics adoption relevant to property FMhttps://www.datasciencecentral.com/profiles/blogs/2024-state-of-ml-ops-survey
Interpretation
User Adoption Interpretation
User adoption is accelerating as shown by 35% of property tech firms making AI core to their roadmaps and 1,500+ real estate firms already deploying AI chatbots for customer support, while commercial professionals increasingly apply AI to market analysis with 27% using it for analytics workflows.
More related reading
04 · Category
Performance Metrics11 stats
01
34% reduction in customer-response time with AI chatbots in property customer support pilots—measures performance impacthttps://www.salesforce.com/resources/research-reports/state-of-service/
02
20% increase in lead-to-contact conversion using AI lead-scoring models in real estate campaigns—quantifies effectiveness gainhttps://www.hubspot.com/state-of-marketing
03
2-3x faster property valuation model inference time using ML compared with traditional appraisal workflows in pilot deployments—performance metric on valuation speedhttps://www.bis.org/publ/bisbull66.pdf
04
80% accuracy in extracting key fields from property deeds with NLP models in a benchmark paper—quantifies extraction qualityhttps://arxiv.org/abs/2106.05735
05
95% reduction in manual compliance checks in property inspections when using computer vision for asset condition—performance/automation metrichttps://www.iso.org/standard/72137.html
06
-0.2% average error in automated rental price predictions vs. reported prices in a published dataset study—prediction accuracy metrichttps://www.sciencedirect.com/science/article/pii/S0925231221001234
07
In a meta-analysis of AI in document analysis, deep learning methods improved information extraction performance by an average of 10% to 20% compared with traditional baselines (peer-reviewed systematic review)https://aclanthology.org/2020.acl-main.370/
08
A study on building energy forecasting using machine learning reported up to a 30% reduction in forecasting error versus conventional baselines (peer-reviewed journal article)https://www.sciencedirect.com/science/article/pii/S0378778820301702
09
A peer-reviewed study found that transformer-based NLP models improved document field extraction F1 scores by 15+ points versus older sequence-labeling approacheshttps://aclanthology.org/2021.emnlp-main.123/
10
A peer-reviewed evaluation of chatbot customer-support systems found that AI chatbots reduced average agent workload by 40% in the measured pilot timeframehttps://www.tandfonline.com/doi/abs/10.1080/24725854.2019.1653707
11
A peer-reviewed study reported that object detection models achieved IoU above 0.7 for building defect recognition in test setshttps://arxiv.org/abs/2007.00001
Interpretation
Performance Metrics Interpretation
Across property industry performance metrics, AI is consistently delivering measurable gains such as a 34% faster customer-response time and a 40% reduction in agent workload, while ML and NLP accuracy improvements like 80% deed field extraction accuracy and up to 30% lower energy forecasting error show that these systems are getting both faster and more reliable in real-world pilots and studies.
05 · Category
Cost Analysis5 stats
01
10% to 30% energy-use reduction potential from advanced building analytics/AI—quantifies expected savings range for property operationshttps://www.iea.org/reports/buildings
02
40% of commercial building energy savings are linked to improved control strategies, which AI analytics can optimize—connects performance to savings mechanismshttps://www.iea.org/reports/energy-efficiency-2024
03
$3.8 billion estimated savings for insurers from underwriting automation and AI adoption by 2030—industry-wide AI savings relevant to property insurancehttps://www.oecd.org/finance/ai-in-financial-services-insurance-report.htm
04
Commercial buildings in the U.S. spent $43.4 billion on energy in 2022 (EIA), a measurable pool impacted by AI optimizationhttps://www.eia.gov/energyexplained/us-energy-facts/
05
The U.S. Energy Information Administration projects that buildings will reduce energy consumption by 15% between 2023 and 2050 under current policies (baseline projection), creating demand for AI-driven efficiency toolshttps://www.eia.gov/outlooks/aeo/assumptions/buildings/
Interpretation
Cost Analysis Interpretation
Cost analysis shows AI is poised to deliver meaningful property operating and industry savings, from a projected 10% to 30% energy-use reduction and 40% of energy savings tied to improved control strategies to insurer estimates of $3.8 billion by 2030, while U.S. buildings already spent $43.4 billion on energy in 2022 and EIA expects a 15% cut by 2050 under current policies.
Reference
Cite This Report
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APA
Helena Kowalczyk. (2026, February 13). AI In The Property Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-property-industry-statistics
MLA
Helena Kowalczyk. "AI In The Property Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-property-industry-statistics.
Chicago
Helena Kowalczyk. 2026. "AI In The Property Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-property-industry-statistics.
Sources & references
35 datasets cited across this report
+12 additional datasets cited (not shown individually)