land data analysis / acquisition pricing research

Charles Lorenz Almenanza

Land Data Analystcomp sheets, blended pricing, county demand research

I turn raw county data into land offers that survive scrutiny. Every price traces back to a specific comp bucket or blended source, never a black box.

Available now Philippines based, works US hours full time or contract

get in touch résumé linkedin

161counties priced and mailed

47,432owner records mailed

10states covered

94counties demand screened

about

Every land offer is only as good as the data behind it. My background is electronics engineering and formal data analytics. Day to day I work in Google Sheets and Apps Script against Redfin, Zamplo, and county GIS data, and I price conservatively on purpose: the goal is an offer that survives scrutiny, not one that chases the highest possible number.

Conservative by default

I price to protect the deal, not to chase the highest possible number.

I show my work

Every price traces back to a specific comp bucket or blended source, never a black box.

Spreadsheet fluent

Comp sheets, pricing models, and QA all built with formulas I can explain line by line.

Detail obsessed

Wrong counties, mislabeled property types, and outliers get caught before they reach pricing.

pricing process

raw county data in, priced offer out

Validate demand

Screen a county's listing volume and sell through rate before committing analysis time to it.

Build the comps

Pull sold land data, group by acreage, drop non arm's length transactions, then set conservative per band pricing.

Price the parcel

Blend comp based pricing with other valuation sources into one defensible price per acre.

Clean and prep

QA owner data, strip non target owners, format for mailing or CRM upload.

land acquisition case studies

Dream Hill Homes Corp · production work

Land Acquisition Pricing Model

161counties priced

47,432owner records mailed

10states covered

Smoothedcontinuous pricing curve

What I built: A repeatable pricing process for vacant land. Sold comparables grouped into acreage bands, conservative benchmark pricing established per band after removing non arm's length sales and statistical outliers, then smoothed into a single curve so price per acre never jumps sharply at a size boundary.

Why it matters: A model that stays consistent across counties means every offer can be explained and defended rather than guessed at, which is what lets it be applied at scale.

Comp analysisFeathered pricing curveGoogle SheetsApps Script

view the full deck

Dream Hill Homes Corp · internal tool, in production

County Pipeline Dashboard

217pipeline runs tracked

57.6%kept rate after scoring

LiveCloudflare Pages and D1

Dailyuse by the acquisitions lead

What I built: A web dashboard on Cloudflare Pages with a D1 database that replaced spreadsheet tracking for the mailing pipeline. County level KPIs, monthly mail volume, lead score dropoff, and kept versus dropped breakdowns, all reading from one database of record. I designed the schema and wrote the aggregation queries behind each KPI tile.

Why it matters: Every number on screen traces back to a record a reviewer can open and check, instead of a snapshot that quietly drifted out of sync across spreadsheet tabs.

Cloudflare PagesD1SQL schema designKPI design

view the one pager

Other analytics work

Eskwelabs capstone · Tableau · team of five

Cuisine Strategy Analysis

Analyzed 5,000 delivery orders (₱1.6M spend) across eight Philippine cities to test whether cuisine strategy should vary by market. It should not, and that was the finding. Chi square on cuisine mix by city was not significant (χ² = 38.02, p = 0.098), a two proportion test on Filipino share urban versus regional was flat (z = 0.55, p = 0.58), and satisfaction showed no relationship to spend or cuisine.

These are failures to reject, not proof of equivalence. The recommendation was to stop debating menu localization, which the data cannot justify, and test operational levers instead.

TableauChi squareProportion testsNull results

view the deck

Eskwelabs capstone · Power BI · team of five

Vietnam Carbon Curve Forecast

Built a Power BI model tracing Vietnam's CO₂ trajectory from 2000 with a forecast to 2030, comparing the emissions trend before and after Power Development Plan VII took effect in 2011.

Coal is the largest single source at roughly 52% of cumulative fossil CO₂ over 2013 to 2023. The central projection lands near 400 MtCO₂ by 2030, with an upper scenario above 500 Mt if coal capacity grows on plan. Those scenarios framed the renewable target and EV adoption options.

Power BITime series forecastingScenario framing

view the deck

experience

Land Data Analyst

Dream Hill Homes Corp May 2025 to present

Built property comp sheets from Redfin sold data and Zamplo or Zillow data to price vacant land parcels for direct mail acquisition campaigns across 161 counties in 10 states.
Validated county level buyer demand before pricing, screening 94 counties on listing volume and sell through rate so analysis time went only where market activity supported it.
Pulled and organized targeted mailing lists in Zamplo based on parcel size, location, and comp derived pricing, covering 47,432 owner records.
Built formula driven Google Sheets and Apps Script workflows, including feathered pricing curves and blended pricing formulas, to standardize comp sheet formatting and speed up turnaround on new county files.
Verified property, owner, and parcel records through county GIS portals (qPublic, ArcGIS).

Process Engineer Intern

Focus Wireless Philippines

Developed Excel dashboards to track yield and downtime.
Analyzed recurring production issues and supported root cause investigations that improved process reliability.
Compiled and organized process data, supporting quality improvement initiatives and decision making.

education

BS Electronics Engineering

Polytechnic University of the Philippines Manila 2020 to 2024

Focused on data analysis, system optimization, and process improvement.

Data Analytics Bootcamp

Eskwelabs Jul to Sep 2025

Applied SQL, Python (pandas, NumPy), Power BI, and Tableau to real world datasets.
Delivered projects in forecasting, dashboard design, and statistical analysis.

pricing and research toolkit

Daily

Google SheetsApps ScriptExcelRedfin ZamploZillowqPublic and ArcGIS

Analysis and reporting

SQLPower BITableauStatistical testing Python (pandas, NumPy)

Domain

Comp analysisCounty demand researchDirect mail list building Parcel record verificationData cleaning and QA

Sheets and Apps ScriptTwo production scripts that build a county comp sheet and a blended pricing file end to end from a raw export.

SQLSchema design and the aggregation queries behind each KPI tile on the pipeline dashboard (Cloudflare D1).

Comp constructionComp sheets across 161 counties, grouped by acreage band and cleaned of non arm's length transactions.

County GISParcel and owner verification through qPublic and ArcGIS portals.

contact

Available for land data analysis and real estate research roles, full time or contract, working US hours from the Philippines.

lorenzalmenanza@gmail.com

linkedin résumé portfolio deck