To calculate asking price per square metre for New Cairo listings, divide each listing’s asking price in Egyptian pounds by its stated area in square metres, then summarize those listing-level results by a cleaned compound name and property type. The result describes the listings collected—not verified sale prices or a definitive “market price.”
What price per square metre measures
For one listing, calculate:
price_per_sqm = asking_price_egp / area_sqm
For example, a listing priced at EGP 5,000,000 with a stated area of 100 m² works out to EGP 50,000/m². This arithmetic example is illustrative, not a New Cairo market observation. Before dividing, confirm that the price is in EGP and that area is recorded in square metres. Area conventions also need to match: built-up area and saleable area are not automatically comparable.
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A compound comparison should keep apartments separate from villas and other property types. It should also show the number of listings, the retrieval date, and some indication of the spread in observed rates. A median of listing-level ratios is less sensitive to unusually high or low listings than a mean, but neither statistic turns asking prices into transaction prices.
What published portal figures can—and cannot—tell you
As context, realestate.eg’s dynamic New Cairo apartments page displayed an average of EGP 90,109/m² and 5,228 available listings in its 2026 overview. The same page’s broader type breakdown listed apartments at EGP 89,889/m², villas at EGP 96,813/m², offices at EGP 111,276/m², and stores at EGP 156,239/m². These are portal-reported figures for the page’s stated scope, not verified sale prices or a compound-by-compound calculation. See realestate.eg’s New Cairo apartments page.
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Aqarmap’s undated compound guide, accessed in 2026, reported an average apartment price of EGP 70,050/m² for compounds in New Cairo. It listed Kattameya Creeks at EGP 183,250/m², Zed East at EGP 152,000/m², Swan Lake Residence at EGP 133,550/m², Cairo Festival City at EGP 123,600/m², and Villette at EGP 116,050/m². The guide does not establish a common as-of date or a transaction-price basis for these figures, so they are best treated as portal context, not directly comparable sale-price estimates. See Aqarmap’s New Cairo compounds price guide.
Knight Frank’s Q1 2025 Cairo residential review offers historical district and property-type context and discusses developer-level differences; it is not a current compound-by-compound listing calculation. No official primary statistical figure for current New Cairo compound-level transaction prices is established here. The Official Egyptian Real Estate Platform describes verified property browsing and mortgage calculation, but the available information does not establish that it provides a downloadable dataset for this analysis. Read Knight Frank’s Q1 2025 Cairo review and visit the Official Egyptian Real Estate Platform.
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Prepare a comparable listing dataset
Use a dataset you are permitted to collect and analyze under its source’s terms. A portal’s displayed inventory count does not mean that its listings are downloadable or that a collected file is complete. Record the source, retrieval date, currency, property-type filter, and area convention alongside the data.
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At minimum, keep fields for the original compound label, property type, asking price in EGP, area in m², and listing date if available. Preserve the original compound spelling so each row can be checked later. For a clean comparison:
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- Convert prices and areas to numeric values; investigate entries that cannot be parsed.
- Exclude or resolve missing, nonpositive, ambiguous, or inconsistent prices and areas.
- Standardize spelling, punctuation, whitespace, transliteration, and phase or project naming before grouping. Basic text cleanup cannot reliably determine whether two differently named projects are the same compound; maintain and review a mapping table for such cases.
- Check that property types and area definitions are comparable, or explain why records were excluded.
- Retain listing dates where possible so stale advertisements do not silently sit alongside recent inventory.
Calculate listing-level rates and summarize by compound
The following example expects a CSV named new_cairo_listings.csv with columns compound, property_type, price_egp, and area_sqm. It validates numeric inputs, removes incomplete or nonpositive records, calculates each listing’s EGP/m², then groups by a basic normalized compound key and property type.
import pandas as pd
# Expected input 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)The code’s whitespace and case cleanup does not resolve spelling variants, Arabic-to-Latin transliteration differences, punctuation, or names that vary by phase. Use a reviewed mapping from original labels to comparison keys for those records, while keeping the original labels in the data. The minimum and maximum in the example make outliers visible but can be distorted by individual listings; percentiles or a distribution plot can provide additional context.
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Pandas describes group-by as “Splitting, applying a function, combining the results.” Its user guide explains the operation and includes groupwise price examples; built-in group operations can be more efficient than a custom apply. The code above uses built-in aggregations. Record the pandas version used when producing a published result; the cited guide is for pandas 3.0.6. Read the pandas group-by guide.
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The example reports the median and mean of the listing-level EGP/m² values. These answer: “What is the typical rate among the listings represented in this group?” The median is usually more robust when a few listings are far above or below the rest; the mean reflects those extremes more strongly.
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- CONFIDENTLY AND EASILY SOLVE: Clients' financial questions whether they're buyers, sellers, investors or renters. Increase your perceived professionalism as a new agent, experienced broker or seasoned loan officer. Close more home sales and impress your clients with fast, accurate answers to all their real estate finance questions from PITI Payments to IRR, NPV and Cashflows
- DEDICATED BUYER QUALIFYING KEYS: Enter client's income, debt and expenses to pre-qualify them to only show properties they can afford. Include tax, insurance and mortgage insurance then compare loan options and payment solutions to give your client choices before they make an offer to buy
- FIGURE OUT THE RIGHT LOAN: For your client at the press of a button for jumbo, conventional, FHA/VA, or even 80:10:10 or 80:15:5 combo loans; check to see if ARMs or bi-weekly loans, quarterly payments or if interest-only payments are the answer; giving your client more choices; easily perform what if loan or TVM calculations find loan amount, term, interest or PITI or PI payments
- BECOME AN INVALUABLE RESOURCE: To your clients by reducing their confusion and uncertainty; ensuring they are able to make a purchase offer; knowing they can afford the down payment; and determining which is the right loan for them. Date-math for listings and contracts too. Comes with a protective slide cover, quick reference guide, pocket user's guide, and long-life battery
A different calculation—total asking price divided by total area—weights larger properties more heavily. It answers a weighted question about the combined advertised inventory, rather than the typical listing’s rate. State which method you use instead of presenting the results as interchangeable.
Apply a minimum listing-count rule before ranking, or place the count next to every result. A compound with one or two observations should not look as well supported as one represented by many listings. A ranking based on sparse or uneven samples can change substantially as listings are added or removed.
Interpret the comparison without overstating it
Describe the output as “asking price per m² among the listings collected,” and give the source and retrieval date. For each compound and property type, readers need enough context to judge comparability:
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- Observation count and a measure of spread, such as a percentile range.
- Property-type definition and area convention.
- Listing recency and the date the data was retrieved.
- Advertised payment terms only when they are captured consistently and matter to the comparison.
Asking price per square metre does not reveal total affordability, fees, delivery or construction status, financing costs, or the price at which a property ultimately closes. A compound ranking on this one measure is not, by itself, a recommendation or a measure of value.
Further pandas reading
For a deeper explanation of pandas aggregation and group operations, Wes McKinney’s Python for Data Analysis, 3rd edition, has a chapter on data aggregation. Read the author-hosted chapter.
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