Work

Ryszard Cieplechowicz: work and writing

Ryszard Cieplechowicz writes about the ordinary details of property-office work: tracking payments, recording calls, organizing documents, and making follow-ups visible.

After more than 17 years in property administration, his published resources focus on small systems an office can actually maintain. They are not legal, accounting, or property-specific advice. They are examples of how a clear record can make the next action less ambiguous.

Start with a usable tool

• Property-management call log template: keep the caller, issue, next owner and promised date together.
• Payment-promise follow-up ledger: distinguish a promise from a posted payment.
• Dropped-task recovery checklist: find a stalled item and hand it to the next person with context.

Those are the canonical versions on the main site. This page points to them rather than republishing their text.

Housing data studies

Beyond office systems, Ryszard Cieplechowicz now publishes reproducible Tampa Bay housing data. The three studies use ACS 2020-2024 five-year estimates for 132 ZCTAs assigned to four counties by largest land-area share, with a minimum of 500 residents each. Every table includes a flag for fewer than 300 estimated units, because small samples are noisy.

The rent burden table combines tenure, median gross rent, median household income and renter cost-burden estimates. It treats "rent burdened" as paying at least 30% of income in gross rent, including utilities, and "severely rent burdened" as at least 50%. The home value table uses ACS tables B25077 and B25075 to report median value and the number of owner-occupied units at or above $500,000. The vacancy table separates seasonal, recreational or occasional units from units listed for rent, since the word "vacant" means different things in the survey than in everyday use.

Each study has a free CSV download, a Kaggle dataset and source code on GitHub so another person can rebuild the table and check it. They describe a survey period. They are not rent quotes, sale prices or forecasts, and they say so on the page.

How the office work and the data work connect

Both come from the same habit: write down what a number means, where it came from and what it cannot tell you. In a property office that habit looks like a call log with a next owner and a due date. In a data table it looks like a README with the source tables, the rounding rule and a stated margin of error.

Where to start

If you work in a property office, start with the call log template and the payment-promise ledger on the main site. If you work with local housing numbers, start with the rent burden study and read the "what the measure means" section before looking at any ZIP area. If you are a student or analyst, the Kaggle datasets are the quickest way to load the tables and try your own questions. All of it is free, and none of it is legal, accounting or property-specific advice.