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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Technology is changing real estate in four connected ways: it is streamlining brokerage work, adding data and automation to property operations, reshaping investment in digital infrastructure, and changing how owners assess climate-related risk. The effects are uneven. A tool or technology-linked asset is valuable only when it fits a real need and its costs, data requirements, skills, power supply and local conditions make sense.
Where technology is changing real estate
Real estate technology—often called proptech—now reaches beyond listing sites and transaction software. It includes tools for agents, data-intensive analysis, connected building systems, construction technology, energy management and the physical infrastructure used by cloud and AI services.
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| Area | What technology can do | What to assess |
|---|---|---|
| Brokerage and transactions | Support listing content, client communication, scheduling, pricing analysis and transaction workflows. | Whether it improves a recurring task or client service enough to justify its cost and learning curve. |
| Property operations | Use connected systems and data to monitor buildings or support predictive maintenance and energy management. | Data quality, integration with existing systems, implementation maturity and the needs of the specific building. |
| Investment and digital infrastructure | Help investors evaluate real estate and support demand for facilities such as data centers. | Power availability, construction constraints, local demand and the difference between market activity and investment returns. |
| Climate and resilience | Help organize information about hazards, building performance and exposure. | Local risks, regulation and data limitations; digital tools do not remove uncertainty or replace property-level due diligence. |
How technology is changing brokerage and transactions
In the United States, the National Association of REALTORS® (NAR) reports that surveyed members use technologies including multiple listing services (MLS), electronic signatures, showing-scheduling tools, comparative market analysis (CMA) or pricing tools, customer relationship management (CRM) systems, drone photography or video, and AI-generated content. Many of these tools address workflow: handling information, arranging viewings, drafting communication or supporting a client through a transaction.
NAR’s 2026 survey gives a more specific picture of agents’ reported technology priorities and AI use. The percentages below describe surveyed REALTORS® and, where marked, only respondents who use AI; they are not rates for all real-estate workers or markets.
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| Survey finding | Reported share | Who the figure describes |
|---|---|---|
| Saving time was a reason for embracing technology | 81% | Surveyed NAR members |
| Improving the client experience was a reason for embracing technology | 71% | Surveyed NAR members |
| Reported using AI daily | 23% | Surveyed NAR members |
| Reported using AI weekly | 25% | Surveyed NAR members |
| Use AI to write listing descriptions | 75% | Surveyed AI users |
| Use AI for social-media posts | 56% | Surveyed AI users |
| Use AI to create emails or follow-up | 52% | Surveyed AI users |
| Identified the learning curve as a major technology-adoption challenge | 63% | Surveyed NAR members |
| Cited cost as a major technology-adoption challenge | 59% | Surveyed NAR members |
These findings point to practical uses rather than proof that AI reliably improves every transaction or raises an agent’s income. They also show why a tool’s apparent convenience is only part of the decision: training and cost can determine whether it fits a brokerage’s work.
NAR’s 2026 technology survey is focused on its US membership. Its figures should not be treated as worldwide adoption rates or as representative of every property profession.
What AI and proptech can—and cannot—tell you
AI applications in real estate range from drafting and communication to analysis of large or recurring data sets. PwC describes organizations moving from exploration and pilots toward measured implementation, with adoption maturity varying across the industry. It identifies predictive maintenance, insurance, mortgage underwriting and construction technology as areas attracting attention because they involve data-rich or repeated decisions.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThose areas are opportunities for application, not evidence that every tool is reliable, widely deployed or commercially mature. Before relying on an AI-supported output, ask what data it uses, whether the information is current and suitable for the property or decision, and who checks the result. For a consequential financial, lending, insurance or building decision, automation is not a substitute for qualified judgment and appropriate verification.
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PwC’s 2026 outlook, based on PwC’s 29th Global CEO Survey, reports that 29% of real-estate CEOs said AI contributed to revenue increases and 32% reported AI-related cost decreases. These are survey responses, not independently established causal effects or a promise that a particular firm will achieve either result.
Proptech also extends into construction, infrastructure, climate, industrial Internet of Things (IoT) and energy. That broader definition matters to property owners and investors: the technology story can involve how a building is operated, how it is built, or what infrastructure and services support it, not just how a property is marketed. PwC’s overview, “Proptech’s Impact on Real Estate Innovation and Transformation,” describes these areas but does not establish a universal product or quantified return from adopting them.
How connected buildings affect operations and energy
Connected building systems can collect information about building conditions and use it to inform operations. Sensors, IoT systems and energy-management tools may be relevant to monitoring, maintenance or energy performance, but their usefulness depends on the building, the quality of the data and how well systems work together. The available evidence does not establish one universally suitable building-control product or a single quantified return from installing sensors.
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Energy performance and resilience also matter because buildings are significant to climate outcomes. The OECD’s 2025 report says buildings account for 37% of global energy-related emissions. This is a sector-wide emissions figure, not a measurement of the effect of any particular technology. For an owner, the practical questions are property-specific: what systems are already in place, what problem needs solving, and what local requirements or operating costs apply?
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Why AI and cloud demand are affecting real estate investment
AI and cloud services need physical infrastructure, including data centers. PwC and the Urban Land Institute’s Emerging Trends in Real Estate 2026 outlook connects growth in AI and cloud demand with data-center demand in the United States and Canada. It reports national data-center vacancy below 2% and says most facilities are pre-leased before completion. Those figures describe the outlook’s region and market period; they are not a guarantee that every proposed facility will be built or profitable.
Power access is a major constraint alongside demand. The outlook notes power shortages and supply bottlenecks, so a site’s access to electricity can affect whether digital-infrastructure construction can proceed. This makes power availability a real-estate consideration, not simply an issue for technology companies.
Investment activity offers another measure of interest, but it should not be confused with broad market growth or predictable returns. McKinsey reports that global real-estate deal value reached $873 billion in 2025, up about 12% year over year, while transaction count remained broadly flat. It also reports that data-center deal volumes rose 37% in 2025. These are market activity figures for that year; rising deal value or deal volume alone does not establish future performance for a particular asset.
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The scale of the underlying asset class is substantial: the OECD’s 2025 report estimates that property assets across 34 OECD countries were worth around USD 111 trillion in 2022, equivalent to 196% of those countries’ combined GDP. This estimate is specific to those countries and that year; it is context for real estate’s economic significance, not a forecast or an estimate of technology’s value.
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For more detail on the regional outlook, see PwC and ULI’s 2026 real-estate trends release. McKinsey’s 2026 overview of real-estate investment reports the global deal figures.
How climate risk and technology intersect
Climate exposure is tied to a property’s location. The OECD identifies physical hazards such as floods, heatwaves and wildfires, as well as transition risks linked to regulation, technology and market expectations. Depending on the asset and its location, these risks may affect usability, value, lending, investment and insurance.
Property data and assessment methods remain inconsistent, so a digital risk score cannot settle every question about a building. Investors, buyers and owners should consider local hazards, building condition and relevant rules, and understand what a tool’s data covers and omits. The OECD’s 2025 report, “Future-proofing Real Estate Investment,” discusses these risks across its research scope; exposure and regulation still need to be assessed in the specific jurisdiction and for the specific asset.
How to judge a real-estate technology decision
Whether you are a buyer assessing a building, a landlord considering an operating system or an investor looking at a technology-linked asset, use the same basic tests before treating technology as an advantage:
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- Identify the actual job. Name the recurring task, operating problem, risk or tenant need the technology is meant to address.
- Check maturity and evidence. Distinguish a deployed tool from a pilot or forecast. Treat user surveys and reported outcomes as such, rather than proof of a causal result.
- Account for cost and learning. Include implementation and training as well as the visible subscription or equipment cost. NAR’s US member survey identifies cost and the learning curve as common adoption challenges.
- Inspect data and integration needs. Ask whether the underlying data are reliable and usable and whether the tool fits existing workflows or building systems.
- Test physical and local constraints. For digital infrastructure, check power and supply constraints; for a building or investment, assess local climate exposure, regulation and asset conditions.
- Compare the expected benefit with the full burden. Do not assume that AI or automation guarantees savings, revenue growth, higher property value or a superior investment return.
What the evidence does not establish
Technology adoption, regulations and product suitability vary by country, profession, building and asset. The NAR survey reflects US members; the cited PwC/ULI data-center outlook focuses on the United States and Canada; and OECD climate findings address risks across its research scope. None of these sources establishes which vendor or hardware model is best, what local rules apply to a particular use, or what return an individual owner or investor will earn.
For technology-linked real estate, the sound conclusion is narrower than “technology makes property better”: it can make particular tasks or assets more workable, but the value depends on evidence, execution and the conditions of the place and property.
PwC’s discussion of how capital and technology may shape transactions is available in “Global M&A trends in real estate and real assets.”
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