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Case · RE/MAX · Real Estate Intelligence

Turning real estate data into an intelligent acquisition and decision system.

The method, flow and agents are presented in a demonstration environment and in the tour (illustrative example). Measured client results are not published on this page.

Illustrative exampleNot published
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01

Executive summary

A national real estate network has fragmented inventory, supply spread across the territory and buyers who say what they want in sentences, not in filters. This case shows how Menlo Tech turns that problem into a system: it interprets intent, crosses it with a geographic graph and with properties, ranks options with explicit criteria and coordinates agents that publish content and learn from the market.

The method, flow and agents are presented in a demonstration environment and in the tour (illustrative example). Measured client results are not published on this page.

Business impact

This case does not publish measured results. The relationships below are design hypotheses, not numbers.

02

Business context

RE/MAX operates as a network: offices and brokers across the country, each with its own portfolio, and a brand that must be found by people looking for a place to live.

For the buyer, the right property depends on place, price, commute, school, neighborhood and routine. For the network, it depends on turning that local knowledge into useful pages and answers, at scale.

03

The operational problem

Search by preset filters (neighborhood, price, bedrooms) misses what people actually say: near the metro, no line changes, school within walking distance, and quiet.

Data is disconnected: properties, geography, search demand and content live in different systems, and every gap becomes manual work or a missed opportunity.

04

Current process and limitations

Before

  • Search by fixed filters, without interpreting intent.
  • Local content produced page by page, with manual review.
  • Inventory, geography and demand in separate databases.

Limitations

  • Compound intents do not fit in filters.
  • Scaling local pages depends on editorial hours.
  • Without a learning loop, what works is not repeated systematically.

05

Data architecture

Properties

Attributes that describe each property and its publication state.

  • Price, area and bedrooms
  • Location and features
  • State: active, in review, published

Geography

Territory entities linked in a graph.

  • Neighborhoods
  • Stations and lines
  • Schools and commute axes

Demand

What people search for and ask.

  • Search queries
  • Suggestions and questions asked to assistants
  • Grouped intents

Behavior

Signals that feed learning.

  • Navigation
  • Contacts
  • Conversion

Real sources and volumes depend on client-approved data. The tour and the demonstration use illustrative data.

06

Mathematical formulation

Semantic retrieval

Illustrative example · Illustrative parameters

Business interpretation

The buyer’s sentence and each property become vectors (embeddings), and similarity measures how close they are. This is how “near the metro” finds properties described in other words.

Variables

  • ssimilarity between query and property (from −1 to 1)
  • qbuyer query
  • pcandidate property
  • Efunction that turns text into a vector

Objective function

Generate candidates: keep the properties closest to the query so they can be ranked afterwards.

Constraints

  • Hard constraints filter first (for example, active listing and region).
  • Similarity generates candidates; it does not decide on its own.

Practical example

A query about an apartment near the metro to get to work brings up properties in neighborhoods with a station, even when the listing does not use those words.

Business implication

Fewer relevant properties stay invisible because of vocabulary.

Assumptions and limitations

Quality depends on the embedding model and on data coverage.

Multi-objective ranking

Illustrative example · Illustrative parameters

Business interpretation

Normalized criteria (location, price, transport, school, comfort, preferences) combine into one score per property.

Variables

  • Ssuitability of property p for query q
  • wᵢweight of criterion i (non-negative; sum to 1)
  • fᵢcriterion i normalized between 0 and 1
  • nnumber of criteria

Objective function

Maximize perceived suitability while respecting hard constraints.

Constraints

  • Hard constraints remove candidates before the sum (for example, no line changes, when the buyer requires it).
  • Weights sum to 1 and may vary with intent.

Practical example

In the demonstration, changing the weight of walkable school reorders the properties and shows how much each criterion contributed to the score.

Business implication

The order stops being opaque: each position has a rationale and explicit trade-offs.

Assumptions and limitations

Weights must be calibrated with conversion evidence; the ones in the example are illustrative.

Constrained optimization

Illustrative example · Illustrative parameters

Business interpretation

Choosing what to publish, highlight or acquire with limited resources is an optimization problem: each action has an expected value and a cost.

Variables

  • xᵢ1 if action i is executed; 0 otherwise
  • vᵢexpected value of action i
  • aᵢeffort or cost of action i
  • bavailable effort budget

Objective function

Maximize the total expected value of the chosen actions.

Constraints

  • The sum of efforts does not exceed the budget.
  • Only actions eligible under policy (quality and portal rules) enter.
  • Each action is either in or out.

Practical example

Among candidate pages, which to publish in this wave given the editorial review limit? The tour shows staged launch with stop criteria.

Business implication

Editorial effort goes where it can return the most.

Assumptions and limitations

The value vᵢ depends on demand forecasting; without measured data it is a hypothesis to validate.

Demand forecasting

Illustrative example · Illustrative parameters

Business interpretation

It combines recent history with the previous forecast to estimate demand in the next periods. Geography and seasonality enter as model extensions.

Variables

  • ŷforecast demand
  • yobserved demand
  • αweight of recent data (between 0 and 1)

Objective function

Minimize forecast error.

Constraints

  • α stays between 0 and 1.
  • Seasonality and geography need their own models, beyond this smoother.

Practical example

If searches for living in a neighborhood rise every start of the year, the forecast anticipates pages and inventory for that period.

Business implication

It prioritizes launches where demand tends to grow.

Assumptions and limitations

Exponential smoothing is the simplest method, used here to explain the idea; the final choice requires validation with real data.

Journeys as a Markov chain

Illustrative example · Illustrative parameters

Business interpretation

Each visit moves through states (search, list, property, contact, conversion). Transition probabilities describe where navigation tends to go.

Variables

  • Xjourney state at step t
  • Pᵢⱼprobability of moving from state i to state j

Objective function

Estimate the probability of conversion and locate where journeys are lost.

Constraints

  • Each row of the transition matrix sums to 1.
  • It assumes the next step depends only on the current state.

Practical example

If many journeys leave the list for outside the site, the priority improvement is in the list, not in the property.

Business implication

The learning loop targets the stage that loses the most.

Assumptions and limitations

It requires navigation data with consent; the example probabilities are not measured.

07

AI and agent architecture

  • Intent AgentTurns the buyer’s sentence into structured intent, with hard constraints and preferences.
  • Property Matching AgentRetrieves candidates by similarity and applies the constraints.
  • Geographic Intelligence AgentQueries the graph for commutes, stations, schools and neighborhood.
  • Content Quality AgentChecks that the page has the required blocks from data and that the text is useful.
  • SEO Validation AgentChecks indexing rules, duplication and file limits.
  • Publication AgentPublishes in waves, within the stop criteria.
  • Performance Analysis AgentTracks indexing and impressions and feeds learning.

Policy layer

Agents propose; a policy layer validates each action before execution. What an agent cannot do, such as publishing without passing quality control, is a rule, not a suggestion.

08

Execution workflow

  1. Natural-language query

    The buyer describes what they want in their own words.

    Illustrative example

  2. Intent classification

    The sentence becomes intents: commute, school, quiet, natural light.

    Illustrative example

  3. Semantic representation

    Query and properties become vectors for comparison.

    Illustrative example

  4. Knowledge graph

    The territory enters as verifiable entities and relations.

    Illustrative example

  5. Property matching

    Candidates are retrieved and filtered by hard constraints.

    Illustrative example

  6. Ranking and optimization

    Criteria combine; trade-offs are explicit.

    Illustrative example

  7. Agent execution

    Agents prepare pages and answers; policy validates.

    Illustrative example

  8. Conversion feedback

    What converts goes back to ranking and prioritization.

    Proposed architecture

09

Interactive demonstration

Synthetic data

From a natural-language request to recommended properties

Write what you are looking for. The demonstration extracts intent, applies constraints, ranks a synthetic catalog with explicit criteria and shows the agents’ trace. None of this is real RE/MAX data.

10

Governance and human oversight

A policy layer validates each agent action before execution: page eligibility, required blocks coming from data, file limits and quality rules.

Human editors review what automation cannot resolve safely and decide exceptions.

Launch is in waves with explicit stop criteria: the next wave only opens if minimum indexing and the rate of pages without impressions stay within the defined limits.

Every decision leaves an event record that makes the path auditable.

11

Operational outcomes

  • Search relevanceCompound intents are understood, not just filtered.Not published
  • Lead qualificationPeople arriving through a specific intent tend to arrive better prepared.Not published
  • Inventory discoveryProperties described in different ways become findable.Not published
  • ConversionMore fitting results should bring search closer to contact.Not published
  • Content operationsLocal pages with blocks coming from data reduce repetitive manual work.Not published
  • Acquisition efficiencyDemand-based prioritization directs the network’s effort.Not published

Each line describes a relationship expected by the design. None is a measured result: no percentages are published for this case.

12

Limitations and assumptions

  • Tour and demonstration data are synthetic or illustrative.
  • No measured client result is claimed.
  • Embeddings, weights and probabilities depend on validation with approved data.
  • Production use depends on the client’s data, privacy and SEO agreements.

13

Technical architecture

  1. Data and knowledge

    Property base, geographic graph and search demand.

    Proposed architecture

  2. Intelligence

    Embeddings, intent classification and forecasting.

    Proposed architecture

  3. Decision

    Multi-objective ranking and constrained optimization.

    Proposed architecture

  4. Orchestration and policy

    Specialized agents and validation of each action.

    Proposed architecture

  5. Observability and learning

    Event log, indexing, impressions and conversion.

    Proposed architecture

Reference architecture: not every component exists in every deployment, and this case does not state which ones are in production.

14

Future opportunities

  • Validate embeddings and weights with approved data and measure relevance.
  • Connect the conversion feedback loop to ranking.
  • Extend demand forecasting with geography and seasonality.
  • Publish measured results when there is authorization and data.

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