The Multifamily Information Transaction Standards (MITS) provide over 3,500 standardized data elements specifically for apartment operations. Created by the National Multifamily Housing Council and now managed by the Real Estate Technology and Transformation Center, MITS has become the de facto language of multifamily technology.

MITS isn't a single standard; it's a family of standards that cover different operational areas. Property-Marketing/ILS 5.0 handles listing syndication and fee transparency. Lease/Application 3.0 standardizes rental agreements. Resident Screening 3.0, Resident Transactions 3.0, and Lead Management 4.0 cover the operational workflows you use daily.

The newest standards emphasize transparency. MITS 5.0 introduced strict requirements for Total Monthly Leasing Price (TMLP), forcing clear disclosure of mandatory versus optional fees no more burying costs in fine print. This transparency will give you the confidence that you're making fair and informed decisions.

AI models need consistent data structures to learn patterns. When your property management system exports MITS-formatted data, it speaks the same language as analytics platforms, marketing tools, and AI applications. Your data becomes instantly more valuable because it works everywhere.

The easiest path: buy MITS-certified software. When evaluating property management systems, ask vendors which MITS standards they support. Most major platforms now offer MITS compliance as a standard feature. If you're building custom integrations, download the free XML schemas and data dictionaries from RETTC's website.

OSCRE International's Industry Data Model offers the most comprehensive real estate standard, encompassing over 130 use cases that cover the entire asset lifecycle. If MITS is purpose-built for apartments, OSCRE is the universal translator for all commercial real estate.

Unlike MITS's transaction focus, OSCRE covers a broader range of services: leasing and space management, facilities management, investment management, portfolio analytics, Building Information Modeling integration, and sustainability tracking. MRI Software became the first primary proptech provider to fully adopt OSCRE IDM in 2023, utilizing it to integrate data across more than 300 partner applications.

Use OSCRE when managing diverse portfolios across various property types, including office, retail, industrial, and mixed-use. Its comprehensive framework makes it ideal for institutional investors and corporate real estate teams managing complex portfolios globally.

The organization actively develops new standards. Current projects include Energy Data Standards and Water Data Standards, which recognize the growing importance of ESG reporting.

The Building Owners and Managers Association's measurement standards directly impact property valuations and lease agreements. Studies show BOMA drawings discover an average 8% more rentable area, meaning properties may be undervalued or under-leased without proper measurement.

BOMA defines how to measure Usable Area (the tenant's private space), Rentable Area (the usable area plus proportional common areas), and four different Gross Area methods for various purposes. Standards exist for office (2017), industrial (2019), retail (2020), mixed-use (2021), and multifamily/hospitality (2023).

That 8% more rentable area isn't theoretical. It's additional leasable square footage you're not charging for. On a 100,000 sq ft building at $30/sq ft, that's $240,000 in annual revenue potentially left on the table.

BOMA standards require hiring certified professionals or training internal staff to ensure compliance. The upfront cost delivers ongoing returns through accurate valuations, fair lease terms, and data consistency that AI models can actually use.

Don't try implementing all three simultaneously. Start with your primary property type: MITS for multifamily, OSCRE for diversified commercial portfolios, BOMA for immediate revenue impact through better measurements.

Standards aren't exciting. They're foundational. Like proper accounting practices or insurance coverage, you don't appreciate them until you need them and by then, it's often too late.

Imagine asking a straightforward question like, "What are the vacant units by property?" or a more advanced one such as, "Show me the overdue charges grouped by aging buckets." Instead of navigating multiple systems and running manual reports, these tools let you type or speak your query in plain language, and the AI-powered system retrieves the exact information you need in seconds. This speed and efficiency will significantly boost your productivity and decision-making process.

This ability to speak to your data represents a game-changing shift in how we engage with information. No specialized training or knowledge of technical systems is required. Ask a question, and the system will interpret it, analyze the underlying data, and deliver precise results. The simplicity of natural language interaction dramatically reduces the time to insights, making data analytics more accessible to everyone, regardless of technical expertise.

Natural Language Processing (NLP) and large language models (LLMs) are at the heart of this transformation. NLP enables machines to understand, interpret, and respond meaningfully to human language. It’s the key to allowing conversations with your data. By processing natural language inputs, NLP-powered systems can comprehend your questions' context, intent, and details.

Meanwhile, LLMs—trained on vast amounts of text and data—allow the AI to understand your queries, retrieve relevant information, and deliver it in a clear, easy-to-understand format. These models are designed to learn from patterns in language to handle everything from simple to highly complex questions with precision.

NLP and LLMs make interacting with your data as easy as speaking to a colleague. The AI agent bridges you and your raw data, converting your plain-language queries into insightful analysis. This empowers you to make faster, data-driven decisions without needing specialized skills.

In one of our recent demos, we showcased how a property management company could ask questions like "Show me the vacant units by property" or "What overdue charges need to be collected, sorted by age?" The system instantly processed these requests and presented detailed responses, breaking down vacancies by property and overdue charges by aging buckets, ensuring the data remained securely housed within its own infrastructure.

This is the next level of data analytics, where NLP and LLMs enable you to *speak directly to your data* and unlock insights faster. By streamlining the process, businesses can focus more on decision-making and less on manual data processing.

Construction tech is one of the hotspots for investors in real estate. In 2018, construction technology saw a massive boost, with venture capitalists investing nearly $3.1 billion in contech startups.  

Contech had a difficult year in 2020 with the pandemic restricting funding events. However, the post-pandemic world only sees an increasing demand for technology. The first half of 2021 saw a massive change with Procore, a construction software company, raising $634.5 million in its IPO. 

What does it mean for the future of the contech industry? Find out below. 

Data is one of the most important assets for any company. You could have the best resources, software, and properties. If you forego data analysis, you’re still leaving your company’s success depending on pure luck. 

You can follow your instinct for many of your decisions. That said, any market and industry includes countless factors—i.e. Competitors, consumers, economics—each one with its set of variables. 

That’s why data science, particularly data analysis, is vital for any company. It provides the right groundwork to nurture informed decisions. That means reliability, boosting your success chances noticeably. 

What does that mean for the real estate industry? How are people using data science? What can you do to add this skill to your roster? That’s what we’ll find out today.