Engine-derived ROI data from 5 representative Charlotte-area properties. Methodology transparent below. CC-BY 4.0, journalists, CPAs, and researchers may cite this dataset with attribution.
Important framing: These are engine outputs for representative fixture scenarios, not predictions about any specific property. The cost segregation engine takes real property data (address, year built, square footage, renovation history, assessor records) and produces a study tailored to your actual property. The aggregate numbers shown here describe the Charlotte market's general profile; your specific results will reflect your specific property.
Each fixture was run through the Cost Seg Smart engine, the same engine that produces real customer studies. Numbers below are reproducible from cities/charlotte.json via scripts/run_city_stats.py.
| Property | Neighborhood | Price | Basis | Land % | 5-yr | 15-yr | Reclass % | Y1 fed savings @ 37% |
|---|---|---|---|---|---|---|---|---|
| Plaza Midwood Bungalow SFR · Built 1928 |
Plaza Midwood / NoDa | $625,000 | $507,250 | 18.8% | $46,617 | $34,613 | 16.0% | $30,055 |
| Dilworth Historic SFR SFR · Built 1922 |
Dilworth | $825,000 | $679,635 | 17.6% | $61,801 | $49,418 | 16.4% | $41,151 |
| South End Condo Investor CONDO · Built 2014 |
South End / SouthPark | $485,000 | $398,524 | 17.8% | $44,914 | $4,042 | 12.3% | $18,114 |
| Ballantyne SFR Rental SFR · Built 2005 |
Ballantyne / Pineville | $425,000 | $344,208 | 19.0% | $34,848 | $22,391 | 16.6% | $21,179 |
| Concord BRRRR Fourplex FOURPLEX · Built 1992 |
University City / Concord (suburban) | $685,000 | $549,850 | 19.7% | $76,405 | $33,166 | 19.9% | $40,541 |
| Engine property type | Fixtures | Median reclass % | Min | Max |
|---|---|---|---|---|
| SFR | 3 | 16.4% | 16.0% | 16.6% |
| CONDO | 1 | 12.3% | 12.3% | 12.3% |
| FOURPLEX | 1 | 19.9% | 19.9% | 19.9% |
"STR" denotes residential property operating as a short-term rental, the engine applies an FF&E density uplift not captured in the LTR (long-term rental) treatment.
| Neighborhood | Typical value | Typical land allocation | Profile note |
|---|---|---|---|
| Plaza Midwood / NoDa | $625,000 | ~28% | Pre-war 1920s bungalow stock heavily renovated post-2010. Strong fix-and-flip and SFR rental activity. Higher land allocation due to neighborhood-scarcity premium. Walking-distance amenity premium. |
| Dilworth | $825,000 | ~30% | Historic streetcar-suburb neighborhood with 1910s–1930s Craftsman and Tudor stock. Highest land allocation in our Charlotte fixtures. Mix of fix-and-flip and SFR rental, some condo conversion. |
| South End / SouthPark | $485,000 | ~24% | Post-2010 mid-rise condo and townhome dominant. New-construction product with cleaner reclassification ratios. Lower land allocation due to vertical density. |
| Ballantyne / Pineville | $425,000 | ~22% | Suburban SFR market south of Charlotte. Lower land allocation. Strong LTR rental cash flow profile. Mecklenburg County (some Pineville town) jurisdiction. |
| University City / Concord (suburban) | $365,000 | ~20% | Lower-cost SFR rental market north and northeast of Charlotte. Lowest land allocation. Strong BRRRR and build-to-rent activity. Cabarrus County (Concord), separate jurisdiction with no Charlotte STR regulation. |
The "typical land allocation" column reflects baseline patterns for each sub-market based on county assessor records and statistical modeling. For specific properties where reconstruction cost (RSMeans 2024 component build-up adjusted for time and geography) exceeds 2.0× the implied depreciable basis after subtracting the baseline land, the engine applies a premium land floor (~50%) to keep the study within audit-defensible territory. This typically affects ultra-premium resort inventory (ski-in/ski-out, beachfront, view-premium properties), where land scarcity premium dominates the purchase price. The per-fixture table above shows the actual land_source used by the engine for each fixture, values of statistical_premium_floor indicate the premium-floor mechanism was applied.
The takeaway: typical neighborhood allocations describe the market baseline. Individual property results depend on specific reconstruction-cost-vs-purchase-price ratios, and ultra-premium product may show higher land allocation in the engine output than the neighborhood typical.
North Carolina partially decouples from federal §168(k). NC historically allows only 85% of federal bonus depreciation in Year 1, with the remaining 15% added back to NC taxable income and recovered over five subsequent years on the state schedule. For 2025+ acquisitions under OBBBA's 100% federal bonus, 15% of the accelerated reclassification dollars hit a NC-side timing mismatch, at NC's 4.5% flat rate, the dollar impact is small but should be modeled into your CPA workflow rather than ignored.
Decoupling: NC's bonus depreciation methodology has been modified multiple times in the past decade. The federal deduction is unaffected; only the NC-side reconciliation timing moves.
State income tax structure: Flat single rate, scheduled rate reductions through 2027+
Verify with your CPA. State tax conformity for federal §168(k) is adjusted frequently. Framing reflects our understanding as of May 2026, verify current-year treatment with a qualified tax professional.
Every figure on this page is reproducible. The pipeline:
cities/charlotte.json under the engine_fixtures array, each with address, property type, purchase price, year built, square footage, and STR/LTR flag.scripts/run_city_stats.py instantiates a PropertyInput for each fixture and calls engine.run_study(), the same path that produces a real customer study.For full methodology details including QC validation, reconciliation logic, and audit-defense documentation, see costsegsmart.com/methodology.
This dataset is licensed under the Creative Commons Attribution 4.0 International License. You may republish, remix, or extend this data for any purpose with attribution. Suggested citation format:
Cost Seg Smart Research Team. (2026). "Charlotte, NC Cost Segregation Benchmarks 2026." Cost Seg Smart. 5 representative fixtures. Retrieved from https://charlottecostseg.com/data/charlotte-cost-seg-stats/
For interview requests, additional data slices, or related questions: [email protected].