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To calculate price per square metre by compound in New Cairo, divide each listing’s asking price in EGP by its stated area in square metres, then group the resulting listing-level values by a reviewed compound name and property type. Report the median, listing count and spread—not just a ranking—and label the result as asking prices from a dated sample, not verified sale prices.
What this calculation tells you—and what it does not
For one listing, the calculation is asking price per sqm = asking price in EGP ÷ area in sqm. If a listing asks EGP 4,000,000 for 100 sqm, its asking price is EGP 40,000 per sqm. That ratio makes listings of different sizes easier to compare, provided their price and area use consistent units and the same kind of area measurement.
When you calculate a median or mean across listings, you are describing the advertised inventory in the dataset you collected. You are not establishing a compound’s verified transaction price or a definitive market value. Listing availability and advertised prices change, so preserve the source and retrieval date with your results.
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Use a dataset you are allowed to collect and analyze under its source’s terms. A portal’s displayed inventory count does not mean that it provides a complete downloadable dataset. Record the source, retrieval date, currency, property-type filter and area convention. Keep the original compound label so that any cleaned or merged names can be checked later.
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A useful input file has at least these columns:
compound: the name as shown on the listingproperty_type: such as apartment or villaprice_egp: the asking price converted to EGParea_sqm: the stated area converted to square metreslisting_date: useful for identifying stale advertisements, if available
Exclude or investigate records with missing or nonnumeric prices and areas, zero or negative values, ambiguous currencies, or incompatible area definitions. In particular, do not treat built-up area and saleable area as interchangeable without checking what each listing means.
Clean compound names before grouping
Spelling differences, transliteration, punctuation, spacing and phase or project names can split one compound into separate groups—or incorrectly combine distinct places. Basic text cleanup helps, but it cannot resolve those identity questions by itself. Preserve the source label and use a reviewed mapping table for names that require judgment.
The example below normalizes case and whitespace for an initial comparison key. It does not decide whether a phase belongs in its parent compound, or whether two differently spelled labels refer to the same development.
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Calculate and summarize with pandas
The example expects a CSV file that you have already obtained lawfully and checked for consistent currency and area conventions. It removes rows with missing or nonpositive numeric values, computes one ratio per listing, then summarizes by compound key and property type.
import pandas as pd
# Expected columns: compound, property_type, price_egp, area_sqm
listings = pd.read_csv("new_cairo_listings.csv")
listings["price_egp"] = pd.to_numeric(listings["price_egp"], errors="coerce")
listings["area_sqm"] = pd.to_numeric(listings["area_sqm"], errors="coerce")
listings = listings.dropna(
subset=["compound", "property_type", "price_egp", "area_sqm"]
)
listings = listings[
(listings["price_egp"] > 0) & (listings["area_sqm"] > 0)
].copy()
listings["compound_key"] = (
listings["compound"]
.str.strip()
.str.casefold()
.str.replace(r"s+", " ", regex=True)
)
listings["price_per_sqm"] = listings["price_egp"] / listings["area_sqm"]
summary = (
listings.groupby(["compound_key", "property_type"])["price_per_sqm"]
.agg(
median_egp_m2="median",
mean_egp_m2="mean",
listings="count",
minimum_egp_m2="min",
maximum_egp_m2="max",
)
.sort_values("median_egp_m2", ascending=False)
)
print(summary)In HTML, the code operators above are escaped for display; in a Python file, write the comparisons as > and & only when those are the literal entities intended. For executable Python, use the operators > and & as rendered characters: > represents >, and & represents &.
The summary includes minimum and maximum values as a simple indication of spread. Those endpoints are sensitive to unusual or erroneous listings, so inspect them rather than treating them as a typical range. You can also report percentiles for a more informative view of dispersion. Record the pandas version used with the result; the pandas 3.0.6 user guide describes group-by as “Splitting, applying a function, combining the results” and explains built-in group operations: pandas: Group by: split-apply-combine.
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Choose an aggregation that answers the right question
The code calculates a ratio for each listing and then summarizes those ratios. The median listing-level ratio gives each listing equal weight and is less affected by a few extreme values than the mean. Neither statistic is automatically a transaction-price estimate.
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A different calculation—total asking price divided by total area—weights larger properties more heavily. It answers a portfolio-style question about the combined asking value and area, not the typical listing’s price per sqm. State which method you used; do not present the two as interchangeable.
Make compound comparisons fair and readable
Separate apartments from villas and other property types. A compound-level figure that mixes them can conceal real differences in property mix. Show the observation count next to every median or mean, and avoid ranking groups with only a handful of records as if they were as reliable as groups with many listings.
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For each comparison, include the features needed to interpret it:
- Property type and the area convention used
- Median listing-level EGP per sqm, with the listing count
- A spread measure or carefully checked range
- Source and retrieval date, plus listing recency where available
- Advertised payment terms only when they are captured consistently and are relevant to the comparison
Price per sqm does not show total purchase affordability, fees, financing costs, construction or delivery status, or the price at which a property ultimately closes. A higher or lower listing median alone is not a recommendation about a compound.
Use portal figures only with their scope and attribution
For context, the dynamic New Cairo apartments page at realestate.eg displayed an average of EGP 90,109 per sqm and 5,228 available listings in its 2026 overview. Its broader property-type breakdown listed apartments at EGP 89,889 per sqm, villas at EGP 96,813, offices at EGP 111,276 and stores at EGP 156,239. These are portal-reported figures for that page’s scope, not verified transaction statistics or a substitute for a dataset with a stated retrieval date.
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An undated Aqarmap guide to compounds in New Cairo, accessed in 2026, reported an average apartment price of EGP 70,050 per sqm. It listed Kattameya Creeks at EGP 183,250 per sqm, Zed East at EGP 152,000, Swan Lake Residence at EGP 133,550, Cairo Festival City at EGP 123,600 and Villette at EGP 116,050. The guide does not establish a shared as-of date or transaction-price basis for those figures, so do not compare them as though they were a same-date, like-for-like calculation.
Knight Frank’s Q1 2025 Cairo residential market review provides historical district and property-type context and discusses developer-level differences; it is not a current compound-by-compound listing calculation. The Official Egyptian Real Estate Platform at realestate.gov.eg describes verified property browsing and mortgage calculation, but it is not established here as a downloadable dataset for this workflow. No official primary statistical figure for current New Cairo compound-level transaction prices was verified.
Quick Recap
Common mistakes to avoid
- Mixing currencies or area units: convert to EGP and sqm before calculating, and verify that the source labels are clear.
- Grouping raw names blindly: normalize obvious formatting differences, then review transliterations and project or phase naming manually.
- Combining property types: compare apartments with apartments and villas with villas unless you explicitly intend to study a mixed portfolio.
- Sorting without counts: a group based on one or two listings is not equally informative as one based on many observations.
- Calling asking prices market prices: identify them as advertised listing values and keep their source date visible.
- Ignoring stale or duplicated listings: check recency and duplicates where the available data allows; a listing count is not necessarily a count of unique, current properties.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
