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1453 Tulane Road

Claremont, CA 91711

Active, $1,385,000

5 beds, 4 baths, 3,359 sq ft living area, 0.25 acre lot, built 1955

Spacious Towne Ranch Pool Retreat. Located on a picturesque tree-lined street, this classic ranch-style home offers a sense of ease and flexibility. A spacious front lawn and mature landscaping set a calm, established tone as you arrive. Inside, natural light moves through the main living areas, highlighting hardwood floors and a layout that encourages connection without sacrificing definition between spaces. The living room is notably spacious, centered around a brick fireplace. Just beyond, the dining room looks out toward the front yard and is framed by a fireplace and built-in details that add character and function. The kitchen is designed for both daily routine and gathering, offering abundant cabinetry and counter space, a built-in electric cooktop, and a Sub-Zero refrigerator seamlessly integrated into the surrounding storage. A casual dining nook provides an easy place for meals with a view toward the front grounds. Adjacent to the kitchen, the family room creates a more relaxed setting, complete with built-in sound system, a reading nook, and direct access to the sunroom. The sunroom, finished with tile flooring, extends the living space with multiple seating areas and a dry bar, offering a comfortable transition between indoors and the backyard. A dedicated office with built-in shelving and storage adds further versatility for work, study, or creative use. The home offers five bedrooms, an office, and four bathrooms. Three bedrooms—including the primary suite—are located on the main level, providing a convenient and adaptable arrangement, while two additional bedrooms upstairs create separation and privacy. One upstairs bedroom opens to its own private deck, adding a quiet outdoor retreat. The primary suite is a sizable retreat within the home, featuring vaulted ceilings and a reading bench at the window overlooking the backyard. The ensuite bath includes dual sinks, a walk-in shower, and a separate soaking tub designed for unhurried moments of relaxation. Out back, the yard is centered around a pool and spa with a natural rock waterfall that becomes a natural focal point. Surrounding lawn areas and mature greenery soften the space, while multiple seating areas allow for lively gatherings or a quiet retreat. With RV potential, a two-car garage, and a layout that adapts easily to a range of living needs, this Towne Ranch home offers space, comfort, and abundant utility in one of Claremont’s most well-established neighborhoods.

Listing Agent: Ryan Zimmerman

Listing Office: CONCIERGE REALTY GROUP

MLS# CV26119911

Hyperlocal Property Analysis

One question matters when you're pricing a home to list, or weighing one to buy: at the price on the table, will it actually sell?

What you've just seen is a citywide analysis of this property, run through my proprietary Sell Odds engine. I built it because the industry has spent decades answering "what's it worth" without measuring the harder question. Sell Odds scores that against thousands of real closings and real failures in the same market. Learn more →

What you're looking at is the citywide layer. The hyperlocal layer analyzes your specific street, your specific tier, and the competition this property is up against. It runs on the same engine but isn't shown publicly, for two reasons.

The first reason is regulatory. Under CRMLS IDX and VOW rules, listings that didn't close (expired, withdrawn, canceled) cannot be shown to the public. They can only be shown to a consumer who has entered into a lawful broker-consumer relationship: personal non-commercial use, no scraping or redistribution, a bona fide interest in the transaction, acknowledgment of MLS ownership. It is enforceable and audited. That failed-listing data is what makes the hyperlocal layer possible, because failure is more than half the picture when you're scoring whether something will sell.

The second reason is analytical. Data alone isn't useful. The discipline applied to it is. Sell Odds runs on appraiser-grade discipline: sold and unsold outcomes only. Active listings are excluded because they are speculative. Pending listings are excluded because they have not closed. What's left is the only empirical record of what the market did and didn't pay. The engine took a year to build to run on that record alone. That's the analysis I walk through in person.

What the in-home analysis reveals:

Adaptive radius, by design. The hyperlocal analysis starts at one mile and expands to two only if the data is defensible enough to score with confidence, applying progressively stricter criteria the further out it has to reach. A mile crosses into different price tiers, different neighborhoods, different competition. Most agents lean on AI tools or CMA software that pulls a random set of properties by bedrooms, baths, and square footage, maybe filtered to a general area. That isn't analysis. That's a mailing list. Adaptive radius scoring is an appraiser's discipline. Agents are not trained in it.
Sell-through rate. None of the large platforms show you this number. It's the percentage of comparable listings that closed versus the ones that didn't. When it falls below sixty percent, more listings are failing than selling. The hyperlocal sell-through rarely matches the citywide. The gap is where the conversation begins.
Months of supply. How much inventory is competing with this property hyperlocally versus citywide. The citywide number averages everything out. The hyperlocal number tells you what's actually in your way. A property can show twelve months of citywide supply and four months hyperlocal. The delta is the leverage.
Price realization. The list-to-sale ratio across actual closings. A hyperlocal market closing at ninety-five percent of list operates very differently from a citywide one closing at eighty-nine. The hyperlocal number tells you where to position the asking price so it doesn't bleed equity through reductions.
Days on market. How fast comparable listings are moving locally versus citywide. Sellers hold the leverage when local days on market run tight. As they extend, that leverage starts moving to the buyer. The citywide number rarely shows either truth.

Four friction drivers, both citywide and hyperlocal, calculated live the moment we sit down. Nothing on Zillow, Redfin, Homes.com, or any other consumer site shows you any of this. Not the failed-listing data. Not the local-vs-citywide delta. Not the discipline. This is what consumer real estate analytics should have looked like a decade ago. Right now, it exists only in one place. Here.

Greg Anderson, REALTOR® · DRE# 01071792
KW Executive · DRE# 02003950
greg@arroyocasa.com · (310) 402-8430
Privacy Policy · Terms of Use

The multiple listing data appearing on this website, or contained in reports produced therefrom, is owned and copyrighted by California Regional Multiple Listing Service, Inc. (“CRMLS”) and is protected by all applicable copyright laws. Information provided is for viewer’s personal, non-commercial use and may not be used for any purpose other than to identify prospective properties the viewer may be interested in purchasing. All listing data, including but not limited to square footage and lot size is believed to be accurate, but the listing Agent, listing Broker and CRMLS and its affiliates do not warrant or guarantee such accuracy. The viewer should independently verify the listed data prior to making any decisions based on such information by personal inspection and/or contacting a real estate professional.

Based on information from California Regional Multiple Listing Service, Inc. and/or other sources. All data, including all measurements and calculations of area, is obtained from various sources and has not been, and will not be, verified by broker or MLS. All information should be independently reviewed and verified for accuracy. Properties may or may not be listed by the office/agent presenting the information.

SellOdds Real Estate Pricing Engine

v7.2

What This Score Represents

This is not an estimate of what your property is worth. It is the probability that your property will sell at its current price — derived entirely from what has actually happened to similar properties in your local market.

The SellOdds v7.2 engine analyzes every comparable sale and every comparable failure within your immediate area. Properties that closed. Properties that expired. Properties that were withdrawn or canceled. It measures where your price falls against that reality and returns a single, calibrated probability score — along with a full price elasticity curve showing how that probability shifts at every price point above and below.

When the score is high, the market data says properties like yours, priced like yours, are selling. When it drops, the data says they aren’t.

How It Is Different

Most pricing tools are automated valuation models — AVMs. They estimate a dollar value for a property based on tax records, public data, and regional averages. They tell you what a home might be worth. They do not tell you whether it will sell.

SellOdds v7.2 is not an AVM. It does not produce a valuation. It answers a fundamentally different question: at this price, what is the likelihood of a successful transaction?

Where traditional models rely on cached data refreshed on weekly or monthly cycles, this engine queries live MLS records at the moment of analysis. Where national platforms apply broad regional averages, this engine locks to your city, your property type, your size segment, and your quality tier. Where other tools treat a lakefront cabin and a fixer on a steep lot as interchangeable data points, this engine reads the listing narrative, classifies features individually, and measures their actual impact on sale outcomes.

There is no pre-trained model. No static index. No algorithmic estimate disconnected from what buyers are actually doing in your market right now.

What Data Powers It

Every calculation begins with live MLS data pulled directly from CRMLS — the largest MLS in the nation. Nothing is cached. Nothing is estimated from tax rolls or public records.

The engine identifies feature-matched comparables using structured MLS fields — bedrooms, bathrooms, living area, property type, geographic coordinates — and then goes further. It reads agent remarks to detect features that have no structured field: lake rights, lakefront access, walk-to-village proximity, remodeled kitchens, level lots, steep terrain, and dozens of other property characteristics that shape buyer behavior.

Comparables are filtered through multiple layers of data integrity checks. Non-residential properties, manufactured homes, vacant land, commercial listings, and lease agreements are excluded. Economically irrelevant outliers — properties so far from the subject’s price range that they would distort the analysis — are removed. Structural anomalies are identified and isolated. Only verified, habitable residential transactions that are genuinely comparable enter the scoring model.

The result is a clean, curated dataset of real outcomes — not a raw data dump averaged into a number.

Full empirical data — including comparable details, feature impact breakdowns, and market leverage analysis — is viewable once you sign in and agree to the VOW IDX compliance terms.

Why It Matters

Pricing a home is the most consequential decision in a real estate transaction. Price too high and the listing stalls — accumulating days on market, attracting lowball offers, and eventually expiring. Price too low and the seller leaves money on the table.

This engine gives agents and sellers something that dollar-value estimates cannot: a direct reading of market tolerance at a specific price point. The interactive price elasticity curve shows exactly how probability responds as price moves — not in theory, but based on the exposed outcomes of every comparable transaction in the local market.

The system also accounts for factors that static models ignore entirely. A property that failed to sell twice before carries that history into the analysis. Extended days on market are detected and reflected. Properties are compared within their own quality tier — an updated home is measured against other updated homes, not averaged against fixers. Size segments are analyzed independently because a 650-square-foot cabin and a 2,200-square-foot home operate in fundamentally different price-per-square-foot realities.

This is pricing intelligence grounded in what the market has actually done — not what an algorithm thinks it should do.

About the SellOdds Engine

SellOdds represents a fundamental shift in real estate pricing — focused on a single objective: translating live market outcomes into actionable pricing intelligence. Built for professionals, the engine is designed for use in listing presentations, pricing strategy sessions, and seller consultations.

The v7.2 engine incorporates dynamic market calibration, quality-tier-aware comparable analysis, feature-level impact measurement, hyperlocal geographic boundaries, and multi-layer data hygiene — all operating against real-time MLS data rather than cached snapshots or pre-computed indices.

SellOdds is not available as a consumer tool. However, Gregory Anderson — developer and proprietor of MountainCribs.com — has integrated the SellOdds engine into this platform as a buyer-facing, added-value feature, bringing professional-grade pricing intelligence directly to the property search experience.

1453 Tulane Road

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