Dearmon AnalyticsAI Solutions & Applied Analytics
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Generative AI consulting · Custom AI solutions · Decision systems

Modeling relationships. Optimizing decisions. Elevating value.

Dearmon Analytics designs generative AI tools, analytical applications, and decision systems for public agencies, financial institutions, educators, nonprofits, and real-estate organizations. Every engagement is principal-led and grounded in rigorous analysis, transparent methods, and human review.

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Engagements
Project and recurring
Established
2015
Based in
Oklahoma City
Illustrative analytical intelligenceForecasts, parcel effects & location intelligence
Forecast + intervalsParcel LMEProperty intelligence
Revenue outlookMonthly · indexed
Synthetic revenue history, baseline forecast, and prediction intervalsObserved values rise unevenly through the historical period. A bright blue baseline forecast continues upward inside narrower 80 percent and wider 95 percent prediction intervals.
ObservedBaseline80% interval95% interval
Parcel-level marginal effectsLME_sqft
Parcel-level map of local square-footage marginal effects. Adjacent parcels range from blue negative effects through white neutral effects to yellow, orange, and red positive effects.

Countywide model · 220,030 prediction parcels · mean estimated sale-price effect $64.55 per added ft²

< −50−50–5050–100100–200200–500> 500
Station-area property intelligenceMarketing view
Illustrative station-area property dashboard with parcel values, transit alignments, filters, and summary metrics.

Parcel values · transit corridors · station-radius filters

Forecast uncertaintyParcel marginal effectsStation-area intelligence
Forecast values are synthetic. The parcel-effect view is from Dearmon & Smith, “A Hierarchical Approach to Scalable Gaussian Process Regression for Spatial Data,” Figure 21(c). The station-area view is a marketing illustration inspired by completed parcel-and-transit dashboard work; organization names and client data have been removed.

Selected project experience

City of DallasCity of Fort WorthCity of Oklahoma CityDallas Area Rapid TransitUniversity of OklahomaCommunity Literacy CentersMidFirst BankBancFirstDevon Energy

Organization names identify project experience only. No endorsement or sponsorship is implied; all trademarks belong to their respective owners.

Approach

AI solutions developed with analytical rigor.

Dearmon Analytics combines generative AI, data engineering, applied economics, econometrics, spatial analysis, and machine learning to build systems that are useful, transparent, documented, and suited to each client's operating context.

About the firm

Applied AI & Analytics Solutions

Explore six focused solution areas.

Explore purpose-built solutions for community-bank stress testing, municipal forecasting, economic intelligence, adaptive education, nonprofit service delivery, and real estate. Each dedicated page explains the decision problem, analytical approach, demonstration, safeguards, and delivery path.

Explore AI solutions Try the live education prototype

Featured banking solution

Rigorous stress testing, right-sized for community banks.

Fixed-fee engagements combine defined deliverables, transparent pricing, and direct principal involvement—without the overhead of a large consulting firm. Move from official Federal Reserve economic scenarios to a defensible model shortlist and documented portfolio results.

View the stress-testing example Request a tailored scope & fee range
Community-bank engagementFixed fee. Defined scope.Transparent pricing · Principal-led · No large-firm overhead
  1. 01
    Economic inputsHistorical, baseline, and severely adverse paths
  2. 02
    Model filterTransformations, lags, signs, fit, and validation
  3. 03
    Decision-ready resultsPortfolio scenarios, diagnostics, and documentation

Final pricing reflects portfolio coverage, data readiness, modeling, validation, documentation, and update requirements. No confidential bank data is needed for an initial conversation.

Capabilities

AI solutions backed by domain expertise.

Each engagement is scoped to the question, available data, reporting calendar, and review requirements.

01

Generative AI & custom solutions

Purpose-built assistants, document and image workflows, browser-based dashboards, and decision systems with human review.

View capability 02

Economic & revenue forecasting

Municipal, transit, and regional forecasts aligned with fiscal calendars, local conditions, and explicit uncertainty.

View capability 03

Stress testing & CECL model support

Historical-series analysis, theory-consistent regression review, scenario testing, benchmarking, and documentation.

View capability 04

Real estate & spatial analytics

Property and housing models that account for geography, neighborhood structure, and submarket effects.

View capability

Selected research

Methods grounded in peer-reviewed scholarship.

Research by Jacob Dearmon and Tony E. Smith at the intersection of spatial econometrics, machine learning, and real estate analytics.

View all research
2025Journal article

A Local Gaussian Process Regression Approach to Mass Appraisal of Residential Properties

The Journal of Real Estate Finance and Economics

View publication
2021Journal article

A hierarchical approach to scalable Gaussian process regression for spatial data

Journal of Spatial Econometrics

View publication
2016Journal article

Gaussian Process Regression and Bayesian Model Averaging: An Alternative Approach to Modeling Spatial Phenomena

Geographical Analysis

View publication

Principal

Jacob T. Dearmon, Ph.D.

An economist whose work spans public finance, banking, real estate, spatial modeling, machine learning, and applied data science.

Education
Ph.D., Economics · B.S., Chemical Engineering
Current role
Professor of Economics and Director of the Ronnie K. Irani Center for Data Analytics and AI at Oklahoma City University
Consulting practice
Founder-led since 2015

Dearmon Analytics LLC is an independent consultancy. Academic affiliations are provided for biographical context.

Full biography

Engagement process

Principal-led from scope through delivery.

Jacob Dearmon remains directly involved throughout each engagement.

  1. 01

    Define the scope

    Establish the question, decision horizon, available data, constraints, audience, and review standard.

  2. 02

    Develop and evaluate

    Compare methods, backtest results, document assumptions, and test alternative scenarios.

  3. 03

    Deliver and communicate

    Provide documentation, briefings, dashboards, or repeatable systems suited to the client workflow.

Contact Dearmon Analytics

Discuss a potential engagement.

Briefly describe the analytical need, available data, timeline, and intended audience. Dearmon Analytics will assess fit and recommend an appropriate scope.

Contact Dearmon Analytics