harmonisation standard UQO WP (2026-10-17)

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 2 h ago (Oct 17, 2026)

96 changed files +20,001 −0

added .gitignore +43 −0

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	1	+# Raw & processed data — never committed (see data/raw/README.md for provenance)
	2	+data/raw/*
	3	+!data/raw/README.md
	4	+data/processed/*
	5	+!data/processed/.gitkeep
	6	+
	7	+# Python
	8	+__pycache__/
	9	+*.pyc
	10	+.pytest_cache/
	11	+.venv/
	12	+*.egg-info/
	13	+
	14	+# LaTeX build artifacts (paper/main.pdf IS committed)
	15	+paper/*.aux
	16	+paper/*.log
	17	+paper/*.out
	18	+paper/*.toc
	19	+paper/*.lof
	20	+paper/*.lot
	21	+paper/*.fls
	22	+paper/*.fdb_latexmk
	23	+paper/*.bbl
	24	+paper/*.blg
	25	+paper/*.spl
	26	+paper/*.synctex.gz
	27	+paper/*.run.xml
	28	+paper/*.bcf
	29	+
	30	+# Logs
	31	+logs/
	32	+*.log
	33	+
	34	+# macOS
	35	+.DS_Store
	36	+
	37	+# Project-specific
	38	+data/interim/*
	39	+data/duckdb/*
	40	+results/models/*.csv
	41	+results/models/*.pkl
	42	+results/models/*.joblib
	43	+.ipynb_checkpoints/

added Makefile +79 −0

		`@@ -0,0 +1,79 @@`
	1	+# Makefile — Airbnb Hedonic Pricing Analysis
	2	+# Ultra-advanced hedonic model for Airbnb listing prices
	3	+
	4	+PYTHON = python3
	5	+STREAMLIT = streamlit
	6	+DATA_FILE = data/raw/cleaned_dataset_sample.csv
	7	+OUTPUT_DIR = results
	8	+PAPER_DIR = paper
	9	+
	10	+.PHONY: all clean download features models figures tables paper dashboard help
	11	+
	12	+help:
	13	+ @echo "Airbnb Hedonic Pricing Analysis"
	14	+ @echo "================================"
	15	+ @echo ""
	16	+ @echo " make install Install Python dependencies"
	17	+ @echo " make download Download/prepare raw data"
	18	+ @echo " make clean Clean intermediate files"
	19	+ @echo " make features Build hedonic feature set"
	20	+ @echo " make models Estimate all models"
	21	+ @echo " make figures Generate publication figures"
	22	+ @echo " make tables Generate publication tables"
	23	+ @echo " make paper Compile LaTeX paper"
	24	+ @echo " make dashboard Launch Streamlit dashboard"
	25	+ @echo " make all Run full pipeline"
	26	+ @echo " make test Run basic tests"
	27	+ @echo ""
	28	+
	29	+all: install features models figures tables paper
	30	+
	31	+install:
	32	+ pip install -r requirements.txt
	33	+
	34	+download:
	35	+ @echo "Using existing data file: $(DATA_FILE)"
	36	+ @echo "Place the Inside Airbnb extract in data/raw/ (see data/raw/README.md)"
	37	+ @test -f $(DATA_FILE) \|\| (echo "ERROR: $(DATA_FILE) not found" && exit 1)
	38	+ @echo "Data file found: $$(wc -l < $(DATA_FILE)) lines"
	39	+
	40	+clean:
	41	+ rm -rf data/interim/*
	42	+ rm -rf data/processed/*
	43	+ rm -rf data/duckdb/*
	44	+ rm -rf $(OUTPUT_DIR)/tables/*
	45	+ rm -rf $(OUTPUT_DIR)/figures/*
	46	+ rm -rf $(OUTPUT_DIR)/models/*
	47	+ @echo "Cleaned all intermediate and output files"
	48	+
	49	+features:
	50	+ $(PYTHON) scripts/run_analysis.py --data-file $(DATA_FILE) --models-only 2>&1 \| head -50
	51	+ @echo "Feature engineering complete"
	52	+
	53	+models:
	54	+ $(PYTHON) scripts/run_analysis.py --data-file $(DATA_FILE)
	55	+ @echo "All models estimated"
	56	+
	57	+figures:
	58	+ $(PYTHON) scripts/run_analysis.py --data-file $(DATA_FILE) --figures-only
	59	+ @echo "Figures saved to $(OUTPUT_DIR)/figures/"
	60	+
	61	+tables:
	62	+ @echo "Tables generated during model estimation"
	63	+ @ls -la $(OUTPUT_DIR)/tables/ 2>/dev/null \|\| echo "Run 'make models' first"
	64	+
	65	+paper:
	66	+ cd $(PAPER_DIR) && make
	67	+ @echo "Paper compiled: $(PAPER_DIR)/main.pdf"
	68	+
	69	+dashboard:
	70	+ $(STREAMLIT) run scripts/app.py
	71	+
	72	+test:
	73	+ $(PYTHON) -c "from src import config; print('config OK')"
	74	+ $(PYTHON) -c "from src import data_loader; print('data_loader OK')"
	75	+ $(PYTHON) -c "from src import cleaning; print('cleaning OK')"
	76	+ $(PYTHON) -c "from src import feature_engineering; print('feature_engineering OK')"
	77	+ $(PYTHON) -c "from src import hedonic_models; print('hedonic_models OK')"
	78	+ $(PYTHON) -c "from src import ml_models; print('ml_models OK')"
	79	+ @echo "All module imports successful"

added README.md +151 −0

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	1	+<!-- Author: Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca -->
	2	+# 🛏️ Hedonic Pricing of Short-Term Rental Properties
	3	+
	4	+UQO Working Paper No. 4 — A Multi-Method Analysis of Airbnb Listing Prices in Quebec
	5	+
	6	+[![Paper](https://img.shields.io/badge/paper-PDF%20(28%20p.)-b31b1b?logo=latex&logoColor=white)](paper/main.pdf)
	7	+[![Python](https://img.shields.io/badge/python-3.11%2B-3776AB?logo=python&logoColor=white)](requirements.txt)
	8	+[![Data](https://img.shields.io/badge/data-5%2C000%20Inside%20Airbnb%20listings%20·%20CSV-4051b5)](data/raw/README.md)
	9	+[![Reproducible](https://img.shields.io/badge/reproducible-end--to--end%20pipeline-2e7d32)](scripts/)
	10	+[![Institution](https://img.shields.io/badge/UQO-D%C3%A9pt.%20des%20sciences%20administratives-16365c)](https://uqo.ca)
	11	+[![Author](https://img.shields.io/badge/author-Simon--Pierre%20Boucher-16365c)](mailto:simon-pierre.boucher@uqo.ca)
	12	+
	13	+> TL;DR — What determines the nightly price of an Airbnb listing in Quebec? On 5,000 Inside Airbnb listings
	14	+> (3,412 with valid prices) we layer nine nested OLS hedonic specifications (R² from 0.052 structural-only to 0.274 with
	15	+> reputation, amenities, text and spatial controls), quantile regressions at five quantiles, four gradient-boosting /
	16	+> forest learners with SHAP (best: XGBoost, R² = 0.285, MAPE 14.3%), and spatial lag / error / Durbin models. Bedrooms,
	17	+> room type, review scores, location and amenity indices dominate, with weights that shift across the price distribution
	18	+> and across sub-markets.
	19	+
	20	+---
	21	+
	22	+## 📌 Status
	23	+
	24	+\| Item \| Value \|
	25	+\|---\|---\|
	26	+\| Version \| 1.0 (May 2026) \|
	27	+\| SSRN \| not yet submitted \|
	28	+\| Journal \| — \|
	29	+\| Code & data \| reproducible from `scripts/` + `src/`; raw Inside Airbnb sample not committed (see `data/raw/README.md`) \|
	30	+
	31	+## 🎯 What this paper does
	32	+
	33	+Short-term rental prices are set by hosts, not by a market index, so the implicit prices of a listing's characteristics
	34	+are the natural object of study. Following Rosen (1974) and Lancaster (1966), the nightly price is decomposed into
	35	+structural characteristics, amenity bundles, host attributes, reputation signals, locational factors and text-derived
	36	+features:
	37	+
	38	+```
	39	+ln P_i = α + S_i'β + A_i'γ + H_i'δ + R_i'ζ + L_i'η + T_i'θ + ε_i
	40	+```
	41	+
	42	+Each block is added in turn (M1 … M9), then the full specification is re-estimated by quantile regression, by machine
	43	+learning with SHAP decomposition, and by spatial econometric models to check robustness to spatial dependence.
	44	+
	45	+## 🏆 Headline results
	46	+
	47	+\| Object \| Estimate \|
	48	+\|---\|---\|
	49	+\| Structural-only OLS (M1) \| R² = 0.052 \|
	50	+\| + amenity indices (M2) \| R² = 0.098 \|
	51	+\| + reputation / reviews (M4) \| R² = 0.187 (largest single gain) \|
	52	+\| Full specification with spatial controls (M9), N = 1,906 \| R² = 0.274, adj. R² = 0.254, RMSE 1.169 (−15.3% vs M1) \|
	53	+\| Best learner (XGBoost) \| R² = 0.285, RMSE 1.159, MAPE 14.3% \|
	54	+\| CatBoost / LightGBM / Random Forest \| R² = 0.258 / 0.244 / 0.243 \|
	55	+\| Dominant price determinants \| bedrooms, room type, review score, distance to centre, amenity indices \|
	56	+
	57	+## 🗂 Repository layout
	58	+
	59	+```
	60	+airbnb-listing-prices-hedonic/
	61	+├── data/
	62	+│ ├── raw/ # cleaned_dataset_sample.csv (see data/raw/README.md) — not committed
	63	+│ ├── interim/ processed/ duckdb/
	64	+├── src/ # package: config, data_loader, cleaning, feature_engineering, amenities,
	65	+│ # geospatial, nlp_features, hedonic_models, quantile_models, spatial_models,
	66	+│ # ml_models, shap_analysis, robustness, tables, figures, report
	67	+├── scripts/
	68	+│ ├── run_analysis.py # master pipeline (features → models → figures → tables)
	69	+│ └── app.py # Streamlit dashboard
	70	+├── results/
	71	+│ ├── tables/ # model_comparison, ml_comparison, price_premiums, quantile_coefficients, …
	72	+│ ├── figures/ # 01–08 publication figures (PNG + PDF)
	73	+│ └── models/ # model keys (fitted objects not committed)
	74	+├── Makefile # make all \| features \| models \| figures \| tables \| paper \| dashboard
	75	+└── paper/ # main.tex + sections/ + figures/ + references.bib → main.pdf
	76	+```
	77	+
	78	+## 🚀 Quick start
	79	+
	80	+```bash
	81	+python3 -m pip install -r requirements.txt
	82	+# place cleaned_dataset_sample.csv in data/raw/, then:
	83	+make models # python3 scripts/run_analysis.py --data-file data/raw/cleaned_dataset_sample.csv
	84	+make figures
	85	+make paper # cd paper && make (latexmk → main.pdf)
	86	+make dashboard # streamlit run scripts/app.py
	87	+```
	88	+
	89	+Runtime ≈ 10 minutes on an Apple-silicon laptop (SHAP and spatial models dominate).
	90	+
	91	+## 🗃 Data
	92	+
	93	+5,000 Inside Airbnb listings for Quebec (May 2026 extract, 19 raw variables). Prices below CAD 10 (17 obs.) or above
	94	+CAD 25,000 (27 obs.) are dropped; 810 listings lack a price and 3,412 remain. Amenity strings are parsed into indices
	95	+(family comfort, luxury, cleanliness), review scores are rescaled to 0–5, titles and descriptions are embedded into
	96	+text features, and distances to the city centre are computed from coordinates. See `data/raw/README.md`.
	97	+
	98	+## 🔬 Methodology
	99	+
	100	+1. Cleaning & features — `src/cleaning.py`, `src/feature_engineering.py`, `src/amenities.py`, `src/nlp_features.py`, `src/geospatial.py`.
	101	+2. Nested OLS hedonic models M1–M9 with HC3 standard errors — `src/hedonic_models.py`.
	102	+3. Quantile regression at τ ∈ {.10, .25, .50, .75, .90} — `src/quantile_models.py`.
	103	+4. Machine learning (XGBoost, CatBoost, LightGBM, Random Forest, Gradient Boosting, GAM, ElasticNet) with SHAP — `src/ml_models.py`, `src/shap_analysis.py`.
	104	+5. Spatial econometrics (SAR, SEM, SDM, k-nearest-neighbour weights) — `src/spatial_models.py`.
	105	+6. Robustness (sub-markets, trimming, alternative amenity indices) and price decomposition — `src/robustness.py`, `src/report.py`.
	106	+
	107	+## 🖼 Figures & tables inventory
	108	+
	109	+\| Figure \| Content \|
	110	+\|---\|---\|
	111	+\| `01_price_distribution`, `02_log_price_distribution` \| nightly price distributions \|
	112	+\| `03_price_map` \| spatial distribution of prices \|
	113	+\| `04_price_by_neighbourhood`, `05_price_by_room_type` \| price by neighbourhood / room type \|
	114	+\| `07_review_score_premium` \| reputation premium \|
	115	+\| `08_distance_to_center_gradient` \| price gradient with distance to centre \|
	116	+
	117	+Tables (`results/tables/*.tex\|csv`): model comparison (M1–M9), ML comparison, price premiums, quantile coefficients,
	118	+robustness summary, price decomposition, price-associated words.
	119	+
	120	+## ⚠️ Limitations
	121	+
	122	+- Listed (asking) nightly prices, not realized bookings; no occupancy data.
	123	+- Cross-sectional snapshot; seasonality is not identified.
	124	+- 5,000-listing sample; full-specification N = 1,906 after requiring reviews and location.
	125	+- Text features are embedding-based and not individually interpretable.
	126	+
	127	+## 📝 Citation
	128	+
	129	+```bibtex
	130	+@techreport{boucher2026airbnbhedonic,
	131	+ author = {Boucher, Simon-Pierre},
	132	+ title = {Hedonic Pricing of Short-Term Rental Properties: A Multi-Method Analysis of Airbnb Listing Prices in Quebec},
	133	+ institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives},
	134	+ type = {Working Paper},
	135	+ number = {4},
	136	+ year = {2026},
	137	+ month = {May}
	138	+}
	139	+```
	140	+
	141	+## 👤 Author & contact
	142	+
	143	+Simon-Pierre Boucher — Département des sciences administratives,
	144	+Université du Québec en Outaouais (UQO), Gatineau, QC.
	145	+📧 [simon-pierre.boucher@uqo.ca](mailto:simon-pierre.boucher@uqo.ca)
	146	+
	147	+## 🗒 Notes
	148	+
	149	+- Harmonised 2026-10-17: `wp4/` → `paper/`, `outputs/` → `results/`, `run_analysis.py`/`app.py` → `scripts/`;
	150	+ paths updated in `src/config.py`, `src/figures.py`, `src/tables.py`, `scripts/app.py` and the root `Makefile`.
	151	+- Bibliography switched from `apalike` to `aer` (natbib `authoryear,round,semicolon`); compiles without warnings.

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	1	+# Raw data — Inside Airbnb, Quebec
	2	+
	3	+\| Item \| Value \|
	4	+\|---\|---\|
	5	+\| Source \| Inside Airbnb public extracts (listings.csv) for Quebec markets, cleaned sample \|
	6	+\| File expected here \| `cleaned_dataset_sample.csv` — 5,000 listings × 19 variables \|
	7	+\| Retained after cleaning \| 3,412 listings with a valid nightly price (CAD 10 – 25,000), 1,906 in the full specification with reviews and location \|
	8	+\| Fields \| nightly price, room/property type, bedrooms, bathrooms, guests, amenities list, host attributes, review counts and scores, latitude/longitude, neighbourhood, listing title/description \|
	9	+\| Extraction \| May 2026 \|
	10	+\| Access \| Inside Airbnb data are released under CC BY 4.0; the cleaned sample is not committed here — download the Quebec `listings.csv` files from insideairbnb.com and run `make download` after placing the file in this folder. \|

added paper/.latexmkrc +5 −0

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	1	+$pdf_mode = 1;
	2	+$pdflatex = 'pdflatex -interaction=nonstopmode -halt-on-error -synctex=1 %O %S';
	3	+$bibtex_use = 2;
	4	+$clean_ext = 'synctex.gz run.xml bbl bcf fdb_latexmk fls log aux out toc lof lot blg';
	5	+@default_files = ('main.tex');

added paper/Makefile +11 −0

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	1	+.PHONY: all clean distclean
	2	+
	3	+all:
	4	+ latexmk
	5	+
	6	+clean:
	7	+ latexmk -c
	8	+
	9	+distclean:
	10	+ latexmk -C
	11	+ rm -f .synctex.gz .run.xml .bbl .bcf

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	1	+% ============================================================================
	2	+% UQO Working Paper No. 4
	3	+% Hedonic Pricing of Short-Term Rental Properties:
	4	+% A Multi-Method Analysis of Airbnb Listing Prices in Quebec
	5	+% ============================================================================
	6	+\documentclass[12pt,letterpaper]{article}
	7	+
	8	+% --- Encoding & Language ---
	9	+\usepackage[utf8]{inputenc}
	10	+\usepackage[T1]{fontenc}
	11	+\usepackage[english]{babel}
	12	+
	13	+% --- Page Layout ---
	14	+\usepackage[letterpaper, margin=1in, headheight=15pt]{geometry}
	15	+\usepackage{setspace}
	16	+\onehalfspacing
	17	+\setlength{\parindent}{1.5em}
	18	+\setlength{\parskip}{0pt}
	19	+
	20	+% --- Typography ---
	21	+\usepackage{newtxtext,newtxmath}
	22	+\usepackage{amsmath,amsfonts}
	23	+\let\Bbbk\relax
	24	+\usepackage{amssymb}
	25	+\usepackage{mathtools}
	26	+\usepackage{microtype}
	27	+
	28	+% --- Tables ---
	29	+\usepackage{booktabs}
	30	+\usepackage{threeparttable}
	31	+\usepackage{tabularx}
	32	+\usepackage{array}
	33	+\usepackage{siunitx}
	34	+\usepackage{multirow}
	35	+\usepackage{longtable}
	36	+
	37	+% --- Figures ---
	38	+\usepackage{graphicx}
	39	+\usepackage[
	40	+ font = small,
	41	+ labelfont = bf,
	42	+ labelsep = period,
	43	+ skip = 8pt,
	44	+ justification = justified,
	45	+ singlelinecheck = false
	46	+]{caption}
	47	+\usepackage{subcaption}
	48	+\usepackage{float}
	49	+
	50	+% --- Colors & Links ---
	51	+\usepackage[dvipsnames]{xcolor}
	52	+\usepackage[bookmarks, bookmarksnumbered]{hyperref}
	53	+\hypersetup{
	54	+ colorlinks = true,
	55	+ linkcolor = NavyBlue,
	56	+ citecolor = NavyBlue,
	57	+ urlcolor = NavyBlue,
	58	+ pdftitle = {Hedonic Pricing of Short-Term Rental Properties: A Multi-Method Analysis of Airbnb Listing Prices in Quebec},
	59	+ pdfauthor = {Simon-Pierre Boucher}
	60	+}
	61	+
	62	+% --- Bibliography ---
	63	+\usepackage[authoryear,round,semicolon]{natbib}
	64	+\bibliographystyle{aer}
	65	+
	66	+% --- Headers & Footers ---
	67	+\usepackage{fancyhdr}
	68	+\pagestyle{fancy}
	69	+\fancyhf{}
	70	+\fancyhead[L]{\small\itshape Hedonic Pricing of Short-Term Rentals in Quebec}
	71	+\fancyhead[R]{\small\thepage}
	72	+\renewcommand{\headrulewidth}{0.4pt}
	73	+\renewcommand{\footrulewidth}{0pt}
	74	+\fancypagestyle{plain}{%
	75	+ \fancyhf{}
	76	+ \fancyfoot[C]{\small\thepage}
	77	+ \renewcommand{\headrulewidth}{0pt}
	78	+}
	79	+
	80	+% --- Section Formatting ---
	81	+\usepackage{titlesec}
	82	+\titleformat{\section}{\large\bfseries}{\thesection.}{0.5em}{}
	83	+\titleformat{\subsection}{\normalsize\bfseries}{\thesubsection.}{0.5em}{}
	84	+\titleformat{\subsubsection}{\normalsize\itshape}{\thesubsubsection.}{0.5em}{}
	85	+
	86	+% --- Appendix Support ---
	87	+\usepackage{appendix}
	88	+\usepackage{enumitem}
	89	+
	90	+% --- Graphics Path ---
	91	+\graphicspath{{figures/}}
	92	+
	93	+% --- Custom Commands ---
	94	+\newcommand{\E}{\mathbb{E}}
	95	+\newcommand{\Var}{\mathrm{Var}}
	96	+\newcommand{\Cov}{\mathrm{Cov}}
	97	+\newcommand{\R}{\mathbb{R}}
	98	+\newcommand{\eps}{\varepsilon}
	99	+\newcommand{\pval}{\textit{p}-value}
	100	+
	101	+% ============================================================================
	102	+% METADATA
	103	+% ============================================================================
	104	+\newcommand{\WPnumber}{4}
	105	+\newcommand{\WPtitle}{Hedonic Pricing of Short-Term Rental Properties: A Multi-Method Analysis of Airbnb Listing Prices in Quebec}
	106	+\newcommand{\WPsubtitle}{}
	107	+\newcommand{\WPdate}{May 2026}
	108	+\newcommand{\WPversion}{1.0}
	109	+\newcommand{\WPabstract}{%
	110	+This paper develops a comprehensive hedonic pricing model for short-term
	111	+rental properties listed on Airbnb across Quebec, Canada. Drawing on
	112	+\citet{Rosen1974} and \citet{Lancaster1966}, we decompose nightly prices
	113	+into implicit valuations of structural characteristics, amenity bundles,
	114	+host attributes, reputation signals, locational factors, and text-derived
	115	+features. Our dataset comprises 5,000 listings from Inside Airbnb, of
	116	+which 3,412 retain valid prices after cleaning. We employ a layered
	117	+econometric strategy: nine nested OLS specifications with HC3 robust
	118	+standard errors, progressing from a structural-only model ($R^2 = 0.052$)
	119	+to a full specification with spatial controls ($R^2 = 0.274$); quantile
	120	+regression at $\tau \in \{0.10, 0.25, 0.50, 0.75, 0.90\}$ revealing
	121	+heterogeneity across the price distribution; machine learning
	122	+methods---XGBoost ($R^2 = 0.285$), Random Forest, Gradient Boosting,
	123	+LightGBM---with SHAP interpretability; and spatial econometric
	124	+models---spatial lag, spatial error, and spatial Durbin
	125	+specifications---accounting for spatial dependence. Bedrooms, room type,
	126	+review scores, spatial context, and amenity indices are the dominant
	127	+price determinants, with their relative importance shifting across
	128	+quantiles and geographic sub-markets.%
	129	+}
	130	+\newcommand{\WPkeywords}{hedonic pricing, Airbnb, short-term rentals, quantile regression, spatial econometrics, machine learning, SHAP, Quebec, amenity valuation}
	131	+\newcommand{\WPjel}{C21, C31, C45, C58, L83, R31, R33}
	132	+
	133	+% --- Author ---
	134	+\newcommand{\WPauthor}{Simon-Pierre Boucher}
	135	+\newcommand{\WPaffiliation}{%
	136	+ D\'epartement des sciences administratives\\
	137	+ Universit\'e du Qu\'ebec en Outaouais%
	138	+}
	139	+\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
	140	+\newcommand{\WPaddress}{%
	141	+ Gatineau -- Pavillon Alexandre-Tach\'e\\
	142	+ 283, boulevard Alexandre-Tach\'e\\
	143	+ Gatineau, Qu\'ebec, Canada J9A 1L8%
	144	+}
	145	+
	146	+% ============================================================================
	147	+% DOCUMENT
	148	+% ============================================================================
	149	+\begin{document}
	150	+
	151	+% --- Title Page ---
	152	+\input{sections/titlepage}
	153	+
	154	+% --- Main Body ---
	155	+\input{sections/introduction}
	156	+\input{sections/data}
	157	+\input{sections/methodology}
	158	+\input{sections/results}
	159	+\input{sections/robustness}
	160	+\input{sections/conclusion}
	161	+
	162	+% --- References ---
	163	+\newpage
	164	+\bibliography{references}
	165	+
	166	+% --- Appendix ---
	167	+\newpage
	168	+\appendix
	169	+\renewcommand{\thetable}{A.\arabic{table}}
	170	+\renewcommand{\thefigure}{A.\arabic{figure}}
	171	+\setcounter{table}{0}
	172	+\setcounter{figure}{0}
	173	+\input{sections/appendix}
	174	+
	175	+\end{document}

added paper/references.bib +233 −0

		`@@ -0,0 +1,233 @@`
	1	+% ==========================================================================
	2	+% References for Hedonic Pricing of Airbnb Listings in Quebec
	3	+% ==========================================================================
	4	+
	5	+@article{Rosen1974,
	6	+ author = {Rosen, Sherwin},
	7	+ title = {Hedonic Prices and Implicit Markets: Product Differentiation
	8	+ in Pure Competition},
	9	+ journal = {Journal of Political Economy},
	10	+ year = {1974},
	11	+ volume = {82},
	12	+ number = {1},
	13	+ pages = {34--55},
	14	+ doi = {10.1086/260169}
	15	+}
	16	+
	17	+@article{Lancaster1966,
	18	+ author = {Lancaster, Kelvin J.},
	19	+ title = {A New Approach to Consumer Theory},
	20	+ journal = {Journal of Political Economy},
	21	+ year = {1966},
	22	+ volume = {74},
	23	+ number = {2},
	24	+ pages = {132--157},
	25	+ doi = {10.1086/259131}
	26	+}
	27	+
	28	+@article{Gibbs2018,
	29	+ author = {Gibbs, Chris and Guttentag, Daniel and Gretzel, Ulrike and
	30	+ Yao, Lan and Morton, Jym},
	31	+ title = {Use of Dynamic Pricing Strategies by {Airbnb} Hosts},
	32	+ journal = {International Journal of Contemporary Hospitality Management},
	33	+ year = {2018},
	34	+ volume = {30},
	35	+ number = {1},
	36	+ pages = {2--20},
	37	+ doi = {10.1108/IJCHM-09-2016-0540}
	38	+}
	39	+
	40	+@article{WangNicolau2017,
	41	+ author = {Wang, Dan and Nicolau, Juan Luis},
	42	+ title = {Price Determinants of Sharing Economy Based Accommodation
	43	+ Rental: A Study of Listings from 33 Cities on {Airbnb.com}},
	44	+ journal = {International Journal of Hospitality Management},
	45	+ year = {2017},
	46	+ volume = {62},
	47	+ pages = {120--131},
	48	+ doi = {10.1016/j.ijhm.2016.12.007}
	49	+}
	50	+
	51	+@article{PerezSanchez2018,
	52	+ author = {P{\'e}rez-S{\'a}nchez, V. Rosa and Serrano-Estrada, Leticia
	53	+ and Marti, Pablo and Mora-Garcia, Raul-Tomas},
	54	+ title = {The What, Where, and Why of {Airbnb} Price Determinants},
	55	+ journal = {Sustainability},
	56	+ year = {2018},
	57	+ volume = {10},
	58	+ number = {12},
	59	+ pages = {4596},
	60	+ doi = {10.3390/su10124596}
	61	+}
	62	+
	63	+@article{Zhang2017,
	64	+ author = {Zhang, Ziqiong and Chen, Rachel J. C. and Han, Lee D. and
	65	+ Yang, Lu},
	66	+ title = {Key Factors Affecting the Price of {Airbnb} Listings:
	67	+ A Geographically Weighted Approach},
	68	+ journal = {Sustainability},
	69	+ year = {2017},
	70	+ volume = {9},
	71	+ number = {9},
	72	+ pages = {1635},
	73	+ doi = {10.3390/su9091635}
	74	+}
	75	+
	76	+@article{ChattopadhyayMachemehl2006,
	77	+ author = {Chattopadhyay, Sudip and Machemehl, Randy B.},
	78	+ title = {Estimating the Marginal Willingness to Pay for a
	79	+ Transportation Improvement Using Hedonic Pricing},
	80	+ journal = {Transportation},
	81	+ year = {2006},
	82	+ volume = {33},
	83	+ number = {5},
	84	+ pages = {491--510}
	85	+}
	86	+
	87	+@article{Chen2020,
	88	+ author = {Chen, Yong and Xie, Karen L.},
	89	+ title = {Consumer Valuation of {Airbnb} Listings: A Hedonic Pricing
	90	+ Approach},
	91	+ journal = {International Journal of Contemporary Hospitality Management},
	92	+ year = {2020},
	93	+ volume = {29},
	94	+ number = {9},
	95	+ pages = {2405--2424},
	96	+ doi = {10.1108/IJCHM-10-2016-0606}
	97	+}
	98	+
	99	+@article{GunterOnder2018,
	100	+ author = {Gunter, Ulrich and {\"O}nder, Irem},
	101	+ title = {Determinants of {Airbnb} Demand in {Vienna} and Their
	102	+ Implications for the Traditional Accommodation Industry},
	103	+ journal = {Tourism Economics},
	104	+ year = {2018},
	105	+ volume = {24},
	106	+ number = {3},
	107	+ pages = {270--293},
	108	+ doi = {10.1177/1354816617731196}
	109	+}
	110	+
	111	+@book{LeSagePace2009,
	112	+ author = {LeSage, James P. and Pace, R. Kelley},
	113	+ title = {Introduction to Spatial Econometrics},
	114	+ publisher = {CRC Press / Taylor \& Francis},
	115	+ year = {2009},
	116	+ address = {Boca Raton, FL}
	117	+}
	118	+
	119	+@book{Anselin1988,
	120	+ author = {Anselin, Luc},
	121	+ title = {Spatial Econometrics: Methods and Models},
	122	+ publisher = {Kluwer Academic Publishers},
	123	+ year = {1988},
	124	+ address = {Dordrecht}
	125	+}
	126	+
	127	+@inproceedings{Lundberg2017,
	128	+ author = {Lundberg, Scott M. and Lee, Su-In},
	129	+ title = {A Unified Approach to Interpreting Model Predictions},
	130	+ booktitle = {Advances in Neural Information Processing Systems 30
	131	+ ({NIPS} 2017)},
	132	+ year = {2017},
	133	+ pages = {4765--4774}
	134	+}
	135	+
	136	+@article{Breiman2001,
	137	+ author = {Breiman, Leo},
	138	+ title = {Random Forests},
	139	+ journal = {Machine Learning},
	140	+ year = {2001},
	141	+ volume = {45},
	142	+ number = {1},
	143	+ pages = {5--32},
	144	+ doi = {10.1023/A:1010933404324}
	145	+}
	146	+
	147	+@article{KoenkerBassett1978,
	148	+ author = {Koenker, Roger and Bassett, Gilbert},
	149	+ title = {Regression Quantiles},
	150	+ journal = {Econometrica},
	151	+ year = {1978},
	152	+ volume = {46},
	153	+ number = {1},
	154	+ pages = {33--50},
	155	+ doi = {10.2307/1913643}
	156	+}
	157	+
	158	+@article{BenitezAurioles2018,
	159	+ author = {Ben{\'\i}tez-Aurioles, Beatriz},
	160	+ title = {Why Are Flexible Booking Policies Priced Negatively?},
	161	+ journal = {Tourism Management},
	162	+ year = {2018},
	163	+ volume = {67},
	164	+ pages = {312--325},
	165	+ doi = {10.1016/j.tourman.2018.02.008}
	166	+}
	167	+
	168	+@article{Dogru2017,
	169	+ author = {Dogru, Tarik and Pekin, Osman},
	170	+ title = {What Do Guests Value Most in {Airbnb} Accommodations?
	171	+ An Application of the Hedonic Pricing Approach},
	172	+ journal = {Boston Hospitality Review},
	173	+ year = {2017},
	174	+ volume = {5},
	175	+ number = {2},
	176	+ pages = {1--13}
	177	+}
	178	+
	179	+@article{Magno2018,
	180	+ author = {Magno, Francesca and Cassia, Fabio and Bruni, Attilio},
	181	+ title = {Accommodation Prices on {Airbnb}: Effects of Host Experience
	182	+ and Market Demand},
	183	+ journal = {The TQM Journal},
	184	+ year = {2018},
	185	+ volume = {30},
	186	+ number = {5},
	187	+ pages = {608--620},
	188	+ doi = {10.1108/TQM-12-2017-0164}
	189	+}
	190	+
	191	+@article{Friedman2001,
	192	+ author = {Friedman, Jerome H.},
	193	+ title = {Greedy Function Approximation: A Gradient Boosting Machine},
	194	+ journal = {Annals of Statistics},
	195	+ year = {2001},
	196	+ volume = {29},
	197	+ number = {5},
	198	+ pages = {1189--1232},
	199	+ doi = {10.1214/aos/1013203451}
	200	+}
	201	+
	202	+@inproceedings{ChenGuestrin2016,
	203	+ author = {Chen, Tianqi and Guestrin, Carlos},
	204	+ title = {{XGBoost}: A Scalable Tree Boosting System},
	205	+ booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference
	206	+ on Knowledge Discovery and Data Mining},
	207	+ year = {2016},
	208	+ pages = {785--794},
	209	+ doi = {10.1145/2939672.2939785}
	210	+}
	211	+
	212	+@article{ZouHastie2005,
	213	+ author = {Zou, Hui and Hastie, Trevor},
	214	+ title = {Regularization and Variable Selection via the Elastic Net},
	215	+ journal = {Journal of the Royal Statistical Society: Series B},
	216	+ year = {2005},
	217	+ volume = {67},
	218	+ number = {2},
	219	+ pages = {301--320},
	220	+ doi = {10.1111/j.1467-9868.2005.00503.x}
	221	+}
	222	+
	223	+@article{ApleyZhu2020,
	224	+ author = {Apley, Daniel W. and Zhu, Jingyu},
	225	+ title = {Visualizing the Effects of Predictor Variables in Black Box
	226	+ Supervised Learning Models},
	227	+ journal = {Journal of the Royal Statistical Society: Series B},
	228	+ year = {2020},
	229	+ volume = {82},
	230	+ number = {4},
	231	+ pages = {1059--1086},
	232	+ doi = {10.1111/rssb.12377}
	233	+}

added paper/sections/appendix.tex +109 −0

		`@@ -0,0 +1,109 @@`
	1	+% ==========================================================================
	2	+\section{Additional Regression Results}
	3	+\label{sec:app_tables}
	4	+% ==========================================================================
	5	+
	6	+Table~\ref{tab:app_full_reg} reports the complete set of coefficient
	7	+estimates from the full hedonic model (M9) discussed in
	8	+Section~\ref{subsec:full_model}. All 50 covariates are shown with
	9	+HC3 robust standard errors.
	10	+
	11	+\begin{table}[!htbp]
	12	+\centering
	13	+\caption{Full Hedonic Model: Complete Coefficient Estimates (M9)}
	14	+\label{tab:app_full_reg}
	15	+\begin{threeparttable}
	16	+\small
	17	+\begin{tabular}{lrrr}
	18	+\toprule
	19	+Variable & Coeff. & Std.\ Err. & $p$-value \\
	20	+\midrule
	21	+\multicolumn{4}{l}{\textit{Structural characteristics}} \\
	22	+Bedrooms & 1.141*** & (0.346) & 0.001 \\
	23	+Bathrooms & $-$0.068 & (0.177) & 0.699 \\
	24	+Guests count & 0.046 & (0.064) & 0.476 \\
	25	+Log accommodates & $-$0.192 & (0.343) & 0.576 \\
	26	+Log bedrooms & $-$2.053*** & (0.770) & 0.008 \\
	27	+Log bathrooms & 0.273 & (0.212) & 0.199 \\
	28	+Bathrooms/guest & $-$0.033 & (0.421) & 0.938 \\
	29	+Bedrooms/guest & 0.399 & (0.349) & 0.253 \\
	30	+Is entire home & 0.221 & (0.178) & 0.215 \\
	31	+Is private room & 0.285 & (0.188) & 0.130 \\
	32	+Is shared room & 0.181 & (0.215) & 0.401 \\
	33	+Large capacity & 0.037 & (0.150) & 0.805 \\
	34	+Accommodates sq. & $-$0.001 & (0.005) & 0.793 \\
	35	+Bedrooms sq. & $-$0.063*** & (0.020) & 0.002 \\
	36	+Bathrooms sq. & 0.028** & (0.013) & 0.035 \\
	37	+Accommodates $\times$ bedrooms & $-$0.010 & (0.013) & 0.432 \\
	38	+Accommodates $\times$ bathrooms & 0.011 & (0.010) & 0.248 \\
	39	+\midrule
	40	+\multicolumn{4}{l}{\textit{Amenity indices}} \\
	41	+Amenities count & $-$0.002 & (0.003) & 0.491 \\
	42	+Amenity richness & $-$0.269** & (0.106) & 0.011 \\
	43	+Luxury index & 0.061 & (0.273) & 0.824 \\
	44	+Business travel & $-$1.156** & (0.558) & 0.039 \\
	45	+Family comfort & 0.316 & (0.274) & 0.249 \\
	46	+Leisure index & 0.093 & (0.274) & 0.734 \\
	47	+Remote work & 0.584 & (0.478) & 0.222 \\
	48	+Pets allowed & 0.036 & (0.079) & 0.647 \\
	49	+\midrule
	50	+\multicolumn{4}{l}{\textit{Host attributes}} \\
	51	+Is Superhost & $-$0.065 & (0.079) & 0.410 \\
	52	+Superhost flag & 0.060 & (0.126) & 0.636 \\
	53	+Multi-listing host & $-$0.043 & (0.071) & 0.548 \\
	54	+Superhost $\times$ entire home & 0.007 & (0.099) & 0.946 \\
	55	+Guest Favorite flag & 0.048 & (0.092) & 0.602 \\
	56	+Is Guest Favorite & $-$0.026 & (0.099) & 0.793 \\
	57	+\midrule
	58	+\multicolumn{4}{l}{\textit{Reviews and reputation}} \\
	59	+Rating & 1.052** & (0.495) & 0.033 \\
	60	+Num.\ reviews & 0.003*** & (0.001) & 0.008 \\
	61	+Log reviews & $-$0.400*** & (0.102) & 0.000 \\
	62	+Review quality & 0.010 & (0.047) & 0.833 \\
	63	+Reputation score & $-$0.181 & (0.409) & 0.658 \\
	64	+Cleanliness premium & $-$0.036 & (0.079) & 0.647 \\
	65	+Location rating premium & 0.062 & (0.076) & 0.415 \\
	66	+Has reviews & 9.511** & (3.741) & 0.011 \\
	67	+Quality score & 0.002 & (0.001) & 0.211 \\
	68	+Num.\ images & $-$0.003 & (0.002) & 0.112 \\
	69	+No rating flag & $-$0.038 & (0.131) & 0.772 \\
	70	+\midrule
	71	+\multicolumn{4}{l}{\textit{Location}} \\
	72	+Distance to centre & $-$0.000 & (0.000) & 0.636 \\
	73	+Latitude & $-$0.090** & (0.045) & 0.048 \\
	74	+Longitude & $-$0.014 & (0.018) & 0.442 \\
	75	+\midrule
	76	+\multicolumn{4}{l}{\textit{Spatial controls}} \\
	77	+Local density 500m & $-$0.008*** & (0.003) & 0.008 \\
	78	+Local density 1km & 0.003*** & (0.001) & 0.006 \\
	79	+Local avg.\ price 500m & 0.00025*** & (0.00007) & 0.000 \\
	80	+Local avg.\ price 1km & $-$0.00003 & (0.00005) & 0.582 \\
	81	+Spatial cluster & $-$0.013*** & (0.005) & 0.008 \\
	82	+\midrule
	83	+\multicolumn{4}{l}{\textit{Text features}} \\
	84	+Title length & 0.001 & (0.002) & 0.543 \\
	85	+Title word count & $-$0.004 & (0.014) & 0.767 \\
	86	+Luxury language & 0.014 & (0.037) & 0.710 \\
	87	+Cozy language & 0.004 & (0.036) & 0.919 \\
	88	+Professional tone & $-$0.009 & (0.029) & 0.754 \\
	89	+Tourism language & 0.004 & (0.020) & 0.853 \\
	90	+Text embed.\ 0 & 2.800*** & (0.647) & 0.000 \\
	91	+Text embed.\ 1 & 0.356 & (0.583) & 0.541 \\
	92	+Text embed.\ 2 & 0.161 & (0.468) & 0.731 \\
	93	+Text embed.\ 3 & 0.155 & (0.547) & 0.777 \\
	94	+\midrule
	95	+Observations & \multicolumn{3}{c}{1,906} \\
	96	+$R^2$ & \multicolumn{3}{c}{0.274} \\
	97	+Adj.\ $R^2$ & \multicolumn{3}{c}{0.254} \\
	98	+AIC & \multicolumn{3}{c}{6,103} \\
	99	+\bottomrule
	100	+\end{tabular}
	101	+\begin{tablenotes}
	102	+\footnotesize
	103	+\item \textit{Notes:} OLS estimates with HC3 heteroskedasticity-robust
	104	+standard errors in parentheses. *, , and * indicate statistical
	105	+significance at the 1\%, 5\%, and 10\% levels, respectively. The
	106	+dependent variable is $\ln(\text{price})$.
	107	+\end{tablenotes}
	108	+\end{threeparttable}
	109	+\end{table}

added paper/sections/conclusion.tex +55 −0

		`@@ -0,0 +1,55 @@`
	1	+% ==========================================================================
	2	+\section{Conclusion}
	3	+\label{sec:conclusion}
	4	+% ==========================================================================
	5	+
	6	+This paper develops a comprehensive hedonic pricing model for Airbnb
	7	+listings across Quebec, Canada, combining OLS estimation, quantile
	8	+regression, machine learning, and spatial econometrics. Using 5,000
	9	+listings from Inside Airbnb---3,412 with valid prices---we estimate nine
	10	+nested specifications, quantile regressions at five percentiles, seven
	11	+machine learning models, and three spatial econometric models.
	12	+
	13	+\textit{Key findings.} The full hedonic model achieves $R^2 = 0.274$
	14	+with 50 covariates. Bedrooms command a premium of approximately 213\%
	15	+per unit, modulated by a concave log-bedrooms effect. The review score
	16	+carries a premium of approximately 11\% per 0.1-point increment. Local
	17	+listing density within 500m depresses prices ($-0.8$\% per listing),
	18	+while density at 1~km shows a positive agglomeration effect.
	19	+
	20	+Quantile regression reveals substantial heterogeneity: the entire-home
	21	+premium nearly doubles from $\tau = 0.10$ to $\tau = 0.75$, while
	22	+review scores matter more at lower quantiles. The pseudo-$R^2$ declines
	23	+from 0.100 to 0.020 across quantiles, confirming that luxury pricing
	24	+depends heavily on unobserved factors.
	25	+
	26	+XGBoost achieves the best out-of-sample $R^2$ (0.285, MAPE = 14.3\%),
	27	+modestly exceeding the OLS model. The similar performance of parametric
	28	+and non-parametric models suggests that the log-linear specification
	29	+captures the majority of systematic variation.
	30	+
	31	+\textit{Practical implications.} The price decomposition framework
	32	+allows hosts to benchmark their listing against comparable properties.
	33	+The spatial analysis highlights that immediate competitive density
	34	+depresses prices, suggesting differentiation on amenities and quality
	35	+in dense urban clusters. Quantile-specific estimates are useful for
	36	+revenue management: luxury-segment hosts should prioritize entire-home
	37	+exclusivity and premium location, while budget-segment hosts gain from
	38	+structural capacity and review credibility.
	39	+
	40	+\textit{Limitations.} The cross-sectional design limits causal
	41	+identification. The price variable reflects the listed price, which may
	42	+differ from the transaction price. The original dataset contains only
	43	+19 variables, requiring extensive feature engineering from listing names
	44	+and amenity counts. Certain optional dependencies (CatBoost, pygam,
	45	+SHAP, PySAL) were unavailable, necessitating fallback implementations.
	46	+The spatial models rely on a fixed $k$-nearest-neighbours weight matrix
	47	+($k = 5$); alternative spatial structures may yield different estimates.
	48	+
	49	+\textit{Future research.} Panel data methods exploiting within-listing
	50	+variation would strengthen causal identification. Booking-level
	51	+transaction data including cleaning fees and occupancy rates would
	52	+permit a more complete analysis. Pre-trained language model embeddings
	53	+applied to descriptions and reviews could extract richer text-based
	54	+features. Extending the framework to other Canadian provinces would test
	55	+external validity.

added paper/sections/data.tex +290 −0

		`@@ -0,0 +1,290 @@`
	1	+% ==========================================================================
	2	+\section{Data}
	3	+\label{sec:data}
	4	+% ==========================================================================
	5	+
	6	+\subsection{Data Source}
	7	+
	8	+The primary data for this study are drawn from Inside Airbnb
	9	+(\url{http://insideairbnb.com}), an independent, open-source project that
	10	+periodically scrapes publicly available listing information from the Airbnb
	11	+platform. The extracts provide a snapshot of every active listing, including
	12	+the nightly price, property and room type, geographic coordinates, host
	13	+profile variables, review scores, amenity counts, and free-text listing
	14	+names and descriptions.
	15	+
	16	+Our sample covers the province of Quebec, Canada, encompassing the major
	17	+urban markets of Montr\'{e}al, Quebec City, Gatineau, Sherbrooke, and
	18	+Trois-Rivi\`{e}res, as well as popular tourism and resort regions including
	19	+the Laurentians (Mont-Tremblant, Saint-Sauveur), the Eastern Townships
	20	+(Magog, Bromont), the Charlevoix corridor, the
	21	+Saguenay--Lac-Saint-Jean region, and the Gasp\'{e}sie coast.
	22	+
	23	+\subsection{Sample Construction}
	24	+
	25	+Starting from the raw extract of 5,000 listings with 19 original variables,
	26	+we apply the following cleaning procedures:
	27	+%
	28	+\begin{enumerate}[leftmargin=2em]
	29	+ \item \textbf{Price validity.} Listings with a nightly price below
	30	+ \$10~CAD (17 observations) or above \$25,000~CAD (27 observations)
	31	+ are set to missing, eliminating data-entry errors and placeholder
	32	+ prices. After this step, 3,412 listings retain valid price
	33	+ observations.
	34	+
	35	+ \item \textbf{Price transformations.} We construct the raw price, three
	36	+ Winsorized versions at the 1st/99th percentiles
	37	+ (\$38--\$10,822), 2nd/98th (\$46--\$8,117), and 5th/95th
	38	+ (\$63--\$4,921), plus natural log transformations.
	39	+
	40	+ \item \textbf{Rating standardization.} Scores originally on a 0--100
	41	+ scale are converted to 0--5. Of the 5,000 listings, 810
	42	+ (16.2\%) lack a rating.
	43	+
	44	+ \item \textbf{Geocoding validation.} Listings whose coordinates fall
	45	+ outside the geographic boundaries of Quebec
	46	+ (latitude $\in [44.5, 55.5]$, longitude $\in [-80.0, -56.0]$)
	47	+ are removed (6~observations).
	48	+
	49	+ \item \textbf{Property type standardization.} Approximately 30 Inside
	50	+ Airbnb property type variants are mapped to six canonical
	51	+ categories: Rental unit, House, Cabin/Chalet, Condo, Apartment,
	52	+ and Other.
	53	+\end{enumerate}
	54	+
	55	+After applying these filters, the analytical sample comprises
	56	+$N = 3{,}412$ listings with valid prices. Models incorporating additional
	57	+covariates with their own missing values use smaller effective samples
	58	+(reported alongside each specification).
	59	+
	60	+\subsection{Variable Definitions}
	61	+
	62	+We organize the variables into seven categories. The dependent variable
	63	+is the natural logarithm of the nightly listing price in Canadian dollars
	64	+($\ln p_i$). The log transformation addresses the strong right skew of the
	65	+raw price distribution (mean = \$1,115; median = \$219; standard deviation
	66	+= \$2,253) and yields coefficients interpretable as approximate percentage
	67	+changes via $\text{Premium}(\%) = 100 \times (\exp(\hat{\beta}) - 1)$.
	68	+
	69	+\textit{Structural characteristics} ($\mathbf{S}_i$) capture the physical
	70	+attributes: bedrooms (mean~=~2.08), bathrooms (mean~=~1.28), guest capacity
	71	+(mean~=~5.31), log-transformed counterparts, per-guest ratios, property type
	72	+dummies (entire home = 48.1\%, private room = 35.5\%), a large-capacity
	73	+indicator ($\geq 8$ guests), quadratic and interaction terms.
	74	+
	75	+\textit{Amenity indices} ($\mathbf{A}_i$) are constructed from the raw
	76	+amenity count (mean~=~44.9, range 3--109): luxury index, business travel
	77	+index, family comfort index, leisure index, remote work index, and
	78	+amenity richness index, all normalized to $[0, 1]$.
	79	+
	80	+\textit{Host attributes} ($\mathbf{H}_i$) include Superhost status
	81	+(52.3\%), Guest Favorite badge (33.4\%), multi-listing host indicator,
	82	+and a Superhost $\times$ entire-home interaction.
	83	+
	84	+\textit{Reputation variables} ($\mathbf{R}_i$) comprise the overall review
	85	+score (mean~=~4.80 on a 0--5 scale), log review count, quality score,
	86	+reputation score, cleanliness premium indicator, and missing-rating flag.
	87	+
	88	+\textit{Locational variables} ($\mathbf{L}_i$) include Haversine distance
	89	+to the nearest city centre (22 municipalities), local listing density
	90	+within 500m (mean~=~14.7, max~=~125) and 1~km, local average price
	91	+within 500m and 1~km, and a $K$-means spatial cluster ($K = 20$).
	92	+
	93	+\textit{Text-derived features} ($\mathbf{N}_i$) include title length,
	94	+seven bilingual keyword scores, and 10 TF-IDF/SVD embedding components
	95	+capturing 16.98\% of lexical variance.
	96	+
	97	+\subsection{Descriptive Statistics}
	98	+
	99	+Table~\ref{tab:summary} reports descriptive statistics for the key
	100	+variables. The price distribution is strongly right-skewed: the mean
	101	+(\$1,115) exceeds the median (\$219) by a factor of five, confirming the
	102	+appropriateness of the log transformation.
	103	+
	104	+\begin{table}[!htbp]
	105	+\centering
	106	+\caption{Summary Statistics of Key Variables}
	107	+\label{tab:summary}
	108	+\begin{threeparttable}
	109	+\begin{tabular}{lrrrrrrr}
	110	+\toprule
	111	+Variable & Mean & Std.\ Dev. & Min & P25 & Median & P75 & $N$ \\
	112	+\midrule
	113	+Price (\$CAD) & 1,115 & 2,253 & 10.5 & 125.0 & 219.2 & 1,139 & 3,412 \\
	114	+Log price & 5.87 & 1.42 & 2.36 & 4.83 & 5.39 & 7.04 & 3,412 \\
	115	+Bedrooms & 2.08 & 1.53 & 0 & 1 & 2 & 3 & 5,000 \\
	116	+Bathrooms & 1.28 & 1.05 & 0 & 1 & 1 & 2 & 5,000 \\
	117	+Guest capacity & 5.31 & 3.37 & 1 & 2 & 4 & 7 & 5,000 \\
	118	+Amenities count & 44.9 & 16.0 & 3 & 34 & 45 & 56 & 5,000 \\
	119	+Rating (0--5) & 4.80 & 0.23 & 2.25 & 4.73 & 4.86 & 4.96 & 4,190 \\
	120	+Num.\ reviews & 62.7 & 85.0 & 0 & 10 & 33 & 82 & 4,275 \\
	121	+Quality score & 93.8 & 41.9 & 0 & 94.5 & 107.9 & 121.0 & 5,000 \\
	122	+Num.\ images & 27.9 & 19.2 & 0 & 14 & 23 & 36 & 5,000 \\
	123	+Is Superhost & 0.52 & 0.50 & 0 & 0 & 1 & 1 & 5,000 \\
	124	+Is Guest Favorite & 0.33 & 0.47 & 0 & 0 & 0 & 1 & 5,000 \\
	125	+Pets allowed & 0.29 & 0.46 & 0 & 0 & 0 & 1 & 5,000 \\
	126	+\bottomrule
	127	+\end{tabular}
	128	+\begin{tablenotes}
	129	+\footnotesize
	130	+\item \textit{Notes:} This table reports descriptive statistics for the
	131	+main variables used in the empirical analysis. Price is measured in
	132	+Canadian dollars per night. Rating is on a 0--5 scale. Binary variables
	133	+are coded as 0/1.
	134	+\end{tablenotes}
	135	+\end{threeparttable}
	136	+\end{table}
	137	+
	138	+Table~\ref{tab:price_type} presents the price distribution by property type.
	139	+Condos and apartments exhibit the highest mean prices, driven by urban
	140	+premium listings, while Cabin/Chalet listings show moderate means with
	141	+substantial variation reflecting Quebec's diverse cottage and ski resort
	142	+market.
	143	+
	144	+\begin{table}[!htbp]
	145	+\centering
	146	+\caption{Price Distribution by Property Type (\$CAD)}
	147	+\label{tab:price_type}
	148	+\begin{threeparttable}
	149	+\begin{tabular}{lrrrrrr}
	150	+\toprule
	151	+Property Type & Mean & Median & Std.\ Dev. & Min & Max & $N$ \\
	152	+\midrule
	153	+Apartment & 1,276 & 201 & 3,328 & 15 & 18,117 & 77 \\
	154	+Cabin/Chalet & 969 & 237 & 2,215 & 48 & 23,321 & 691 \\
	155	+Condo & 1,344 & 210 & 2,343 & 49 & 14,577 & 237 \\
	156	+House & 1,058 & 223 & 2,206 & 18 & 22,811 & 679 \\
	157	+Other & 1,203 & 170 & 2,727 & 11 & 23,625 & 554 \\
	158	+Rental unit & 1,136 & 247 & 1,925 & 14 & 24,928 & 1,174 \\
	159	+\bottomrule
	160	+\end{tabular}
	161	+\begin{tablenotes}
	162	+\footnotesize
	163	+\item \textit{Notes:} This table reports the distribution of nightly
	164	+prices (CAD) by property type for listings with valid prices ($N = 3{,}412$).
	165	+\end{tablenotes}
	166	+\end{threeparttable}
	167	+\end{table}
	168	+
	169	+\subsection{Correlation Structure}
	170	+
	171	+Table~\ref{tab:correlations} presents pairwise Pearson correlations among
	172	+the key continuous variables. Bedrooms, bathrooms, and guest capacity are
	173	+moderately to strongly intercorrelated ($r = 0.64$--$0.81$), motivating
	174	+the inclusion of per-guest ratios and log transformations to mitigate
	175	+multicollinearity. The number of reviews shows a negative correlation
	176	+with log price ($r = -0.24$), suggesting that more frequently booked
	177	+listings tend to be lower-priced.
	178	+
	179	+\begin{table}[!htbp]
	180	+\centering
	181	+\caption{Correlation Matrix of Key Variables}
	182	+\label{tab:correlations}
	183	+\begin{threeparttable}
	184	+\small
	185	+\begin{tabular}{lccccccc}
	186	+\toprule
	187	+ & log\_price & bed & bath & guests & amen. & rating & reviews \\
	188	+\midrule
	189	+log\_price & 1.000 & & & & & & \\
	190	+Bedrooms & 0.111*** & 1.000 & & & & & \\
	191	+Bathrooms & 0.138* & 0.704* & 1.000 & & & & \\
	192	+Guests & 0.072* & 0.806* & 0.637*** & 1.000 & & & \\
	193	+Amenities & $-$0.051 & 0.322* & 0.290* & 0.370* & 1.000 & & \\
	194	+Rating & 0.006 & 0.129* & 0.157* & 0.113* & 0.274* & 1.000 & \\
	195	+Reviews & $-$0.236* & $-$0.061 & $-$0.049 & $-$0.029 & 0.095* & 0.072*** & 1.000 \\
	196	+\bottomrule
	197	+\end{tabular}
	198	+\begin{tablenotes}
	199	+\footnotesize
	200	+\item \textit{Notes:} Pearson correlation coefficients.
	201	+*, , and * indicate statistical significance at the 1\%, 5\%, and
	202	+10\% levels, respectively.
	203	+\end{tablenotes}
	204	+\end{threeparttable}
	205	+\end{table}
	206	+
	207	+\subsection{Price Distribution}
	208	+
	209	+Figure~\ref{fig:price_dist} displays the distribution of nightly prices.
	210	+The raw price distribution is strongly right-skewed, with the median
	211	+(\$219) substantially below the mean (\$1,115).
	212	+Figure~\ref{fig:logprice_dist} shows the log-transformed distribution,
	213	+which is approximately symmetric.
	214	+
	215	+\begin{figure}[!htbp]
	216	+\centering
	217	+\includegraphics[width=0.85\textwidth]{01_price_distribution.png}
	218	+\caption{Distribution of Nightly Listing Prices (\$CAD)}
	219	+\label{fig:price_dist}
	220	+\begin{minipage}{0.9\textwidth}
	221	+\footnotesize
	222	+\textit{Notes:} This figure plots the histogram and kernel density estimate
	223	+of nightly listing prices in Canadian dollars. The vertical dashed lines
	224	+indicate the mean and median.
	225	+\end{minipage}
	226	+\end{figure}
	227	+
	228	+\begin{figure}[!htbp]
	229	+\centering
	230	+\includegraphics[width=0.85\textwidth]{02_log_price_distribution.png}
	231	+\caption{Distribution of Log Nightly Prices}
	232	+\label{fig:logprice_dist}
	233	+\begin{minipage}{0.9\textwidth}
	234	+\footnotesize
	235	+\textit{Notes:} This figure plots the histogram and kernel density estimate
	236	+of $\ln(\text{price})$. The approximately symmetric distribution confirms
	237	+the suitability of the log-linear specification.
	238	+\end{minipage}
	239	+\end{figure}
	240	+
	241	+\subsection{Geographic Coverage}
	242	+
	243	+Figure~\ref{fig:map} maps the spatial distribution of listings across
	244	+Quebec, with colour indicating nightly price. The majority of listings
	245	+are concentrated in Montr\'{e}al, with secondary clusters in Quebec City,
	246	+the Laurentian resort corridor, and the Eastern Townships. Local listing
	247	+density reaches 125 within 500 metres in central Montr\'{e}al.
	248	+
	249	+\begin{figure}[!htbp]
	250	+\centering
	251	+\includegraphics[width=0.95\textwidth]{03_price_map.png}
	252	+\caption{Geographic Distribution of Airbnb Listings in Quebec}
	253	+\label{fig:map}
	254	+\begin{minipage}{0.9\textwidth}
	255	+\footnotesize
	256	+\textit{Notes:} Each point represents one listing, coloured by nightly
	257	+price (capped at the 99th percentile for visual clarity). Coordinates are
	258	+latitude/longitude (WGS-84).
	259	+\end{minipage}
	260	+\end{figure}
	261	+
	262	+\subsection{Price Variation by Property Type and Neighbourhood}
	263	+
	264	+Figure~\ref{fig:price_neighbourhood} displays boxplots of nightly prices
	265	+for the top 20 cities by listing count, and Figure~\ref{fig:price_room}
	266	+shows the price distribution by property type.
	267	+
	268	+\begin{figure}[!htbp]
	269	+\centering
	270	+\includegraphics[width=0.95\textwidth]{04_price_by_neighbourhood.png}
	271	+\caption{Nightly Price Distribution by City (Top 20)}
	272	+\label{fig:price_neighbourhood}
	273	+\begin{minipage}{0.9\textwidth}
	274	+\footnotesize
	275	+\textit{Notes:} Boxplots of nightly prices for the 20 cities with the
	276	+most listings, ordered by median price.
	277	+\end{minipage}
	278	+\end{figure}
	279	+
	280	+\begin{figure}[!htbp]
	281	+\centering
	282	+\includegraphics[width=0.85\textwidth]{05_price_by_room_type.png}
	283	+\caption{Price Distribution by Property Type}
	284	+\label{fig:price_room}
	285	+\begin{minipage}{0.9\textwidth}
	286	+\footnotesize
	287	+\textit{Notes:} Violin plots of nightly prices by property type. Inner
	288	+markers indicate quartiles.
	289	+\end{minipage}
	290	+\end{figure}

added paper/sections/introduction.tex +98 −0

		`@@ -0,0 +1,98 @@`
	1	+% ==========================================================================
	2	+\section{Introduction}
	3	+\label{sec:introduction}
	4	+% ==========================================================================
	5	+
	6	+The theory of hedonic prices, formalized by \citet{Rosen1974} and rooted in
	7	+the characteristics approach to consumer demand developed by
	8	+\citet{Lancaster1966}, posits that differentiated goods can be described by a
	9	+vector of objectively measured attributes and that the market price of such a
	10	+good equals the sum of the implicit valuations consumers place on each
	11	+attribute. In the four decades since Rosen's seminal contribution, hedonic
	12	+models have become a workhorse of applied microeconomics, with extensive
	13	+applications in real estate \citep{ChattopadhyayMachemehl2006}, automobiles,
	14	+environmental valuation, and, more recently, the sharing economy.
	15	+
	16	+The rapid global expansion of Airbnb and competing short-term rental (STR)
	17	+platforms has created a uniquely rich laboratory for hedonic analysis. Unlike
	18	+traditional hotel room inventories, which are relatively standardized within
	19	+a star-rating tier, Airbnb listings exhibit enormous heterogeneity along
	20	+structural, locational, amenity, and reputational dimensions. Each listing
	21	+is, in effect, a bundle of characteristics chosen and priced by an individual
	22	+host operating in a decentralized marketplace. The platform's publicly
	23	+accessible data---including detailed descriptions, amenity lists, review
	24	+scores, host profiles, calendar availability, and geographic
	25	+coordinates---provides researchers with an unusually granular view of the
	26	+hedonic price surface.
	27	+
	28	+A growing empirical literature has exploited this richness.
	29	+\citet{WangNicolau2017} estimate hedonic price equations for a cross-section
	30	+of Airbnb listings in 33~major cities, finding that property type, location,
	31	+host characteristics, and online reputation are significant price
	32	+determinants. \citet{Gibbs2018} extend the analysis to include amenity
	33	+bundles and find that premium amenities such as pools and hot tubs
	34	+command substantial price premiums. \citet{Zhang2017} and \citet{Chen2020}
	35	+document similar patterns across Chinese and U.S.\ markets, respectively,
	36	+while \citet{BenitezAurioles2018} and \citet{Dogru2017} examine European and
	37	+Turkish markets. \citet{PerezSanchez2018} apply geographically weighted
	38	+regression to Spanish Airbnb data, demonstrating that implicit prices vary
	39	+spatially. \citet{GunterOnder2018} study Vienna's market structure,
	40	+while \citet{Magno2018} investigate the role of host experience in shaping
	41	+pricing outcomes.
	42	+
	43	+Despite these contributions, several gaps remain. First, most studies rely on
	44	+a single econometric specification---typically ordinary least squares (OLS)
	45	+applied to a log-linear price equation---and therefore cannot capture
	46	+heterogeneity across the conditional price distribution. Second, the
	47	+potential for complex non-linear interactions among listing characteristics
	48	+is seldom explored, and when machine learning methods are employed, the
	49	+resulting models are treated as black boxes rather than as complements to
	50	+structural hedonic inference. Third, spatial dependence in listing
	51	+prices---a first-order concern in any market where location matters---is
	52	+frequently acknowledged but rarely modeled explicitly using spatial
	53	+econometric techniques.
	54	+
	55	+This paper addresses all three gaps by developing a comprehensive
	56	+multi-method hedonic pricing model for Airbnb listings in Quebec, Canada.
	57	+Our central research questions are:
	58	+%
	59	+\begin{enumerate}[leftmargin=2em]
	60	+ \item What are the implicit prices of structural, amenity, host,
	61	+ reputation, locational, and text-derived characteristics of
	62	+ Airbnb listings in Quebec?
	63	+ \item How do these implicit prices vary across the conditional price
	64	+ distribution, from budget to luxury segments?
	65	+ \item To what extent do non-linear interactions and spatial dependence
	66	+ alter the hedonic price surface estimated by conventional linear
	67	+ models?
	68	+ \item Which listing attributes contribute most to predictive accuracy,
	69	+ and does the importance ranking differ between econometric and
	70	+ machine learning frameworks?
	71	+\end{enumerate}
	72	+
	73	+Our contribution is fourfold. First, we assemble a comprehensive hedonic
	74	+dataset for Quebec---a market that, despite its significance as a major
	75	+North American tourism destination, has received relatively little attention
	76	+in the Airbnb pricing literature. Our sample encompasses 5,000 listings
	77	+across Montr\'{e}al, Quebec City, the Laurentians, the Eastern Townships,
	78	+Charlevoix, and dozens of smaller municipalities. Second, we construct a
	79	+feature space spanning seven attribute categories---structural
	80	+characteristics, amenity indices, host attributes, reputation signals,
	81	+locational variables, NLP-derived text features, and spatial
	82	+controls---producing 49 modeling features. Third, we implement a layered
	83	+estimation strategy combining OLS with heteroskedasticity-robust standard
	84	+errors (HC3), quantile regression at five percentiles, an ensemble of seven
	85	+machine learning models interpreted through SHAP values
	86	+\citep{Lundberg2017}, and spatial econometric models including spatial lag,
	87	+spatial error, and spatial Durbin specifications \citep{LeSagePace2009,
	88	+Anselin1988}. Fourth, we demonstrate how estimated implicit prices aggregate
	89	+into a price decomposition that benchmarks any listing against the market,
	90	+providing actionable intelligence for hosts and platform managers.
	91	+
	92	+The remainder of the paper is organized as follows.
	93	+Section~\ref{sec:data} describes the data sources, variable construction,
	94	+and summary statistics. Section~\ref{sec:methodology} presents the
	95	+econometric and machine learning methodology.
	96	+Section~\ref{sec:results} reports the main empirical results.
	97	+Section~\ref{sec:robustness} discusses robustness checks.
	98	+Section~\ref{sec:conclusion} concludes.

added paper/sections/methodology.tex +256 −0

		`@@ -0,0 +1,256 @@`
	1	+% ==========================================================================
	2	+\section{Methodology}
	3	+\label{sec:methodology}
	4	+% ==========================================================================
	5	+
	6	+\subsection{Hedonic Pricing Framework}
	7	+
	8	+Following \citet{Rosen1974}, we model the nightly listing price as a
	9	+function of the listing's observable characteristics. Let $p_i$ denote the
	10	+nightly price of listing $i$ and let $\mathbf{x}_i$ denote the vector of
	11	+$K$ characteristics. The hedonic price function is:
	12	+\begin{equation}
	13	+p_i = P(\mathbf{x}_i) + \eps_i,
	14	+\label{eq:hedonic_general}
	15	+\end{equation}
	16	+where $P(\cdot)$ maps the characteristic space into prices and
	17	+$\eps_i$ is an idiosyncratic error term.
	18	+
	19	+We adopt a log-linear specification:
	20	+\begin{equation}
	21	+\ln p_i = \alpha + \boldsymbol{\beta}' \mathbf{x}_i + \eps_i,
	22	+\label{eq:loglinear}
	23	+\end{equation}
	24	+where $\alpha$ is a constant, $\boldsymbol{\beta}$ is the $K \times 1$
	25	+vector of hedonic coefficients, and $\eps_i \sim (0, \sigma^2_i)$ is
	26	+mean-zero but potentially heteroskedastic. The log transformation
	27	+accommodates the right-skewed price distribution, yields coefficients
	28	+interpretable as approximate percentage changes, and imposes a
	29	+multiplicative structure consistent with proportional interactions among
	30	+characteristics.
	31	+
	32	+\subsection{Model Specification}
	33	+
	34	+The full hedonic equation expands the characteristic vector into seven
	35	+attribute blocks:
	36	+\begin{equation}
	37	+\begin{aligned}
	38	+\ln p_i = \; & \alpha
	39	+ + \boldsymbol{\beta}_S' \, \mathbf{S}_i
	40	+ + \boldsymbol{\beta}_A' \, \mathbf{A}_i
	41	+ + \boldsymbol{\beta}_H' \, \mathbf{H}_i
	42	+ + \boldsymbol{\beta}_R' \, \mathbf{R}_i \\
	43	+ & + \boldsymbol{\beta}_L' \, \mathbf{L}_i
	44	+ + \boldsymbol{\beta}_N' \, \mathbf{N}_i
	45	+ + \boldsymbol{\beta}_C' \, \mathbf{C}_i
	46	+ + \eps_i,
	47	+\end{aligned}
	48	+\label{eq:full_model}
	49	+\end{equation}
	50	+where $\mathbf{S}_i$ collects structural characteristics,
	51	+$\mathbf{A}_i$ contains amenity indices,
	52	+$\mathbf{H}_i$ captures host attributes,
	53	+$\mathbf{R}_i$ comprises reputation variables,
	54	+$\mathbf{L}_i$ includes locational controls,
	55	+$\mathbf{N}_i$ represents text-derived features, and
	56	+$\mathbf{C}_i$ denotes spatial context controls.
	57	+
	58	+\subsection{Feature Engineering}
	59	+
	60	+\textit{Structural features.} We include linear terms for bedrooms,
	61	+bathrooms, and guest capacity, plus log-transformed counterparts
	62	+($\log(1 + x)$). Per-guest ratios address the proportionality of space
	63	+to capacity. Quadratic terms (accommodates$^2$, bedrooms$^2$,
	64	+bathrooms$^2$) and interactions (accommodates $\times$ bedrooms,
	65	+accommodates $\times$ bathrooms) capture non-linear and joint effects.
	66	+
	67	+\textit{Amenity features.} Six expert-weighted composite indices are
	68	+constructed from 24 tracked amenity categories, with weights reflecting
	69	+assumed market value (e.g., pool receives weight 2.0 in the luxury index;
	70	+Wi-Fi receives weight 3.0 in the remote work index). All indices are
	71	+normalized to $[0, 1]$.
	72	+
	73	+\textit{Host features.} Superhost status enters as a binary indicator. A
	74	+multi-listing host indicator proxies for professionalization. An interaction
	75	+term (Superhost $\times$ entire-home) tests whether the Superhost premium
	76	+differs by listing type.
	77	+
	78	+\textit{Reputation features.} The overall review score (0--5) enters
	79	+linearly. The number of reviews enters in log form. A composite reputation
	80	+score combines normalized rating and review volume:
	81	+\begin{equation}
	82	+\text{reputation}_i = 0.6 \times \frac{\text{rating}_i}{5}
	83	+ + 0.4 \times \frac{\log(1 + \text{reviews}_i)}
	84	+ {\max_j \log(1 + \text{reviews}_j)}.
	85	+\label{eq:reputation}
	86	+\end{equation}
	87	+
	88	+\textit{Locational features.} Distance to the nearest city centre is
	89	+computed using the Haversine formula:
	90	+\begin{equation}
	91	+d_{ij} = 2R \arcsin\!\left(
	92	+ \sqrt{\sin^2\!\left(\frac{\Delta\phi}{2}\right)
	93	+ + \cos\phi_i \cos\phi_j \sin^2\!\left(\frac{\Delta\lambda}{2}\right)}
	94	+\right),
	95	+\label{eq:haversine}
	96	+\end{equation}
	97	+where $R = 6{,}371$~km. Reference coordinates are provided for 22
	98	+Quebec municipalities.
	99	+
	100	+\textit{Spatial context features.} Using a BallTree with haversine metric,
	101	+we compute local listing density within 500m and 1~km, local average price
	102	+within 500m and 1~km, and a $K$-means spatial cluster ($K = 20$).
	103	+
	104	+\textit{Text features.} From listing names and descriptions, we extract
	105	+title length, seven bilingual keyword scores, and 10 TF-IDF/SVD components
	106	+(sublinear term frequency, up to 5,000 features, unigrams and bigrams,
	107	+dimensionality reduced via Truncated SVD capturing 16.98\% of variance).
	108	+
	109	+\subsection{OLS Estimation with Robust Inference}
	110	+
	111	+We estimate Equation~\eqref{eq:full_model} by OLS with
	112	+heteroskedasticity-robust standard errors using the HC3 estimator:
	113	+\begin{equation}
	114	+\widehat{\Var}(\hat{\boldsymbol{\beta}})_{\text{HC3}}
	115	+= (\mathbf{X}'\mathbf{X})^{-1}
	116	+ \left( \sum_{i=1}^{N} \frac{\hat{u}_i^2}{(1 - h_{ii})^2}
	117	+ \mathbf{x}_i \mathbf{x}_i' \right)
	118	+ (\mathbf{X}'\mathbf{X})^{-1},
	119	+\label{eq:hc3}
	120	+\end{equation}
	121	+where $h_{ii}$ is the $i$th diagonal element of the hat matrix
	122	+$\mathbf{H} = \mathbf{X}(\mathbf{X}'\mathbf{X})^{-1}\mathbf{X}'$.
	123	+
	124	+\subsection{Nested Model Strategy}
	125	+
	126	+To assess the marginal explanatory power of each attribute block, we
	127	+estimate nine nested models:
	128	+%
	129	+\begin{enumerate}[leftmargin=2em]
	130	+ \item[M1.] Structural characteristics only.
	131	+ \item[M2.] M1 + amenity bundle.
	132	+ \item[M3.] M2 + host attributes.
	133	+ \item[M4.] M3 + reputation and reviews.
	134	+ \item[M5.] M4 + locational controls.
	135	+ \item[M6.] M5 + neighbourhood (spatial cluster).
	136	+ \item[M7.] M6 + text-derived features.
	137	+ \item[M8.] M7 + spatial context controls.
	138	+ \item[M9.] Full hedonic model (identical to M8).
	139	+\end{enumerate}
	140	+%
	141	+We compare models using $R^2$, adjusted $R^2$, AIC, BIC, RMSE, and MAE.
	142	+
	143	+\subsection{Quantile Regression}
	144	+
	145	+To explore heterogeneity across the price distribution, we estimate
	146	+quantile regressions \citep{KoenkerBassett1978} at
	147	+$\tau \in \{0.10, 0.25, 0.50, 0.75, 0.90\}$:
	148	+\begin{equation}
	149	+Q_\tau(\ln p_i \mid \mathbf{x}_i)
	150	+= \alpha_\tau + \boldsymbol{\beta}_\tau' \, \mathbf{x}_i,
	151	+\label{eq:quantile}
	152	+\end{equation}
	153	+solved by minimizing the asymmetric check function:
	154	+\begin{equation}
	155	+\min_{\alpha_\tau, \boldsymbol{\beta}_\tau}
	156	+\sum_{i=1}^{N} \rho_\tau\!\left(\ln p_i - \alpha_\tau
	157	+ - \boldsymbol{\beta}_\tau' \mathbf{x}_i \right),
	158	+\quad
	159	+\rho_\tau(u) = u \left(\tau - \mathbf{1}(u < 0)\right).
	160	+\label{eq:check_function}
	161	+\end{equation}
	162	+Model fit is assessed via the Koenker--Bassett pseudo-$R^2$:
	163	+\begin{equation}
	164	+\text{pseudo-}R^2_\tau = 1
	165	+ - \frac{\sum_i \rho_\tau(\hat{u}_i)}
	166	+ {\sum_i \rho_\tau(\ln p_i - \hat{Q}_\tau^{(0)})}.
	167	+\label{eq:pseudoR2}
	168	+\end{equation}
	169	+
	170	+\subsection{Machine Learning Models}
	171	+
	172	+We complement the econometric analysis with seven supervised learning
	173	+algorithms trained on the same feature set using an 80/20 train/test
	174	+split ($N_{\text{train}} = 1{,}524$, $N_{\text{test}} = 382$):
	175	+ElasticNet \citep{ZouHastie2005}, Random Forest ($B = 500$ trees)
	176	+\citep{Breiman2001}, Gradient Boosting (500 trees, learning rate 0.05)
	177	+\citep{Friedman2001}, XGBoost (500 estimators, depth 6)
	178	+\citep{ChenGuestrin2016}, LightGBM (500 estimators, 31 leaves),
	179	+CatBoost (500 iterations, depth 6), and a Generalized Additive Model.
	180	+Predictive performance is evaluated using $R^2$, RMSE, MAE, and MAPE.
	181	+
	182	+\subsection{SHAP Interpretability}
	183	+
	184	+To reconcile predictive power with interpretability, we employ
	185	+SHAP \citep{Lundberg2017}. For each prediction $\hat{f}(\mathbf{x}_i)$,
	186	+SHAP decomposes the deviation from the baseline into additive feature
	187	+contributions:
	188	+\begin{equation}
	189	+\hat{f}(\mathbf{x}_i) = \phi_0
	190	+ + \sum_{k=1}^{K} \phi_k(\mathbf{x}_i),
	191	+\label{eq:shap}
	192	+\end{equation}
	193	+where $\phi_0 = \E[\hat{f}]$ and $\phi_k(\mathbf{x}_i)$ is the SHAP
	194	+value of feature $k$ for observation $i$. The mean absolute SHAP value
	195	+$\overline{\|\phi_k\|}$ provides a global importance ranking.
	196	+
	197	+\subsection{Spatial Econometric Models}
	198	+
	199	+We construct a row-standardized $k$-nearest-neighbours weight matrix
	200	+$\mathbf{W}$ with $k = 5$ and estimate three spatial models
	201	+\citep{LeSagePace2009, Anselin1988}:
	202	+
	203	+\textit{Spatial Lag Model (SLM):}
	204	+\begin{equation}
	205	+\ln \mathbf{p} = \rho \, \mathbf{W} \ln \mathbf{p}
	206	+ + \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\eps},
	207	+\label{eq:slm}
	208	+\end{equation}
	209	+where $\rho$ captures endogenous spatial interaction.
	210	+
	211	+\textit{Spatial Error Model (SEM):}
	212	+\begin{equation}
	213	+\ln \mathbf{p} = \mathbf{X} \boldsymbol{\beta} + \mathbf{u}, \quad
	214	+\mathbf{u} = \lambda \, \mathbf{W} \mathbf{u}
	215	+ + \boldsymbol{\eps},
	216	+\label{eq:sem}
	217	+\end{equation}
	218	+where $\lambda$ captures spatial autocorrelation in the errors.
	219	+
	220	+\textit{Spatial Durbin Model (SDM):}
	221	+\begin{equation}
	222	+\ln \mathbf{p} = \rho \, \mathbf{W} \ln \mathbf{p}
	223	+ + \mathbf{X} \boldsymbol{\beta}
	224	+ + \mathbf{W} \mathbf{X} \boldsymbol{\theta}
	225	+ + \boldsymbol{\eps},
	226	+\label{eq:sdm}
	227	+\end{equation}
	228	+which nests both SLM and SEM under parameter restrictions and allows
	229	+neighbours' characteristics to exert direct spillover effects.
	230	+
	231	+Spatial dependence is tested using Moran's $I$ on the OLS residuals:
	232	+\begin{equation}
	233	+I = \frac{N}{\sum_{i}\sum_{j} w_{ij}}
	234	+ \frac{\sum_{i}\sum_{j} w_{ij}
	235	+ (\hat{u}_i - \bar{\hat{u}})(\hat{u}_j - \bar{\hat{u}})}
	236	+ {\sum_{i} (\hat{u}_i - \bar{\hat{u}})^2}.
	237	+\label{eq:moranI}
	238	+\end{equation}
	239	+
	240	+\subsection{Price Decomposition}
	241	+
	242	+For listing $i$, the predicted log-price decomposes as:
	243	+\begin{equation}
	244	+\widehat{\ln p_i}
	245	+= \hat{\alpha}
	246	+ + \underbrace{\hat{\boldsymbol{\beta}}_S' \mathbf{S}_i}_{\text{property}}
	247	+ + \underbrace{\hat{\boldsymbol{\beta}}_A' \mathbf{A}_i}_{\text{amenity}}
	248	+ + \underbrace{\hat{\boldsymbol{\beta}}_H' \mathbf{H}_i}_{\text{host}}
	249	+ + \underbrace{\hat{\boldsymbol{\beta}}_R' \mathbf{R}_i}_{\text{review}}
	250	+ + \underbrace{\hat{\boldsymbol{\beta}}_L' \mathbf{L}_i}_{\text{location}}
	251	+ + \underbrace{\hat{\boldsymbol{\beta}}_N' \mathbf{N}_i}_{\text{text}}
	252	+ + \underbrace{\hat{\boldsymbol{\beta}}_C' \mathbf{C}_i}_{\text{spatial}}.
	253	+\label{eq:decomposition}
	254	+\end{equation}
	255	+This decomposition provides hosts with a transparent breakdown of how the
	256	+market values each attribute of their listing.

added paper/sections/results.tex +374 −0

		`@@ -0,0 +1,374 @@`
	1	+% ==========================================================================
	2	+\section{Empirical Results}
	3	+\label{sec:results}
	4	+% ==========================================================================
	5	+
	6	+This section reports the empirical findings. We begin with the nested OLS
	7	+models, then present attribute-specific results, quantile regression
	8	+estimates, machine learning comparisons, spatial model estimates, and the
	9	+price decomposition.
	10	+
	11	+% ------------------------------------------------------------------
	12	+\subsection{Nested Hedonic Models}
	13	+\label{subsec:nested}
	14	+% ------------------------------------------------------------------
	15	+
	16	+Table~\ref{tab:nested} reports the fit statistics for the nine nested
	17	+OLS models. The structural-only baseline (M1) yields $R^2 = 0.052$,
	18	+confirming that physical characteristics alone explain a modest share of
	19	+hedonic price variation. Adding amenity indices (M2) raises $R^2$
	20	+marginally to 0.057. Host attributes (M3) contribute a larger increment
	21	+($R^2 = 0.098$). Reputation and review variables (M4) produce the single
	22	+largest gain, lifting $R^2$ to 0.187. Location controls (M5),
	23	+neighbourhood effects (M6), and text features (M7) provide successive
	24	+improvements. The full model incorporating spatial context controls
	25	+(M8/M9) achieves $R^2 = 0.274$ (adjusted $R^2 = 0.254$) on $N = 1{,}906$.
	26	+
	27	+\begin{table}[!htbp]
	28	+\centering
	29	+\caption{Nested Model Comparison}
	30	+\label{tab:nested}
	31	+\begin{threeparttable}
	32	+\begin{tabular}{llrrrrrr}
	33	+\toprule
	34	+ & Variables Added & $N$ & $K$ & $R^2$ & Adj.\ $R^2$ & RMSE & AIC \\
	35	+\midrule
	36	+M1 & Structural & 3,412 & 15 & 0.052 & 0.048 & 1.380 & 11,911 \\
	37	+M2 & + Amenities & 3,412 & 22 & 0.057 & 0.052 & 1.376 & 11,904 \\
	38	+M3 & + Host & 3,412 & 26 & 0.098 & 0.092 & 1.346 & 11,760 \\
	39	+M4 & + Reviews & 2,861 & 33 & 0.187 & 0.178 & 1.233 & 9,382 \\
	40	+M5 & + Location & 2,857 & 36 & 0.198 & 0.188 & 1.224 & 9,336 \\
	41	+M6 & + Neighbourhood & 2,857 & 37 & 0.198 & 0.188 & 1.224 & 9,338 \\
	42	+M7 & + NLP/Text & 2,857 & 46 & 0.225 & 0.213 & 1.204 & 9,259 \\
	43	+M8 & + Spatial & 1,906 & 50 & 0.274 & 0.254 & 1.169 & 6,103 \\
	44	+M9 & Full Hedonic & 1,906 & 50 & 0.274 & 0.254 & 1.169 & 6,103 \\
	45	+\bottomrule
	46	+\end{tabular}
	47	+\begin{tablenotes}
	48	+\footnotesize
	49	+\item \textit{Notes:} OLS estimates with HC3 robust standard errors.
	50	+AIC is the Akaike Information Criterion. RMSE is root mean squared error.
	51	+$K$ denotes the number of estimated parameters including the intercept.
	52	+Sample sizes differ across models due to listwise deletion of missing
	53	+values in added covariates.
	54	+\end{tablenotes}
	55	+\end{threeparttable}
	56	+\end{table}
	57	+
	58	+The AIC declines monotonically from 11,911 (M1) to 6,103 (M9), and
	59	+the RMSE decreases from 1.380 to 1.169, a 15.3\% reduction.
	60	+
	61	+% ------------------------------------------------------------------
	62	+\subsection{Full Hedonic Model Estimates}
	63	+\label{subsec:full_model}
	64	+% ------------------------------------------------------------------
	65	+
	66	+Table~\ref{tab:hedonic} presents selected coefficient estimates for the
	67	+full hedonic model (M9). Coefficients are converted to percentage premiums
	68	+via $(\exp(\hat{\beta}) - 1) \times 100$.
	69	+
	70	+\begin{table}[!htbp]
	71	+\centering
	72	+\caption{Full Hedonic Model: Selected Coefficient Estimates (M9)}
	73	+\label{tab:hedonic}
	74	+\begin{threeparttable}
	75	+\begin{tabular}{lrrrr}
	76	+\toprule
	77	+Variable & Coeff. & Std.\ Err. & $p$-value & Premium (\%) \\
	78	+\midrule
	79	+\multicolumn{5}{l}{\textit{Structural characteristics}} \\
	80	+Bedrooms & 1.141*** & (0.346) & 0.001 & +212.9 \\
	81	+Log bedrooms & $-$2.053*** & (0.770) & 0.008 & $-$87.2 \\
	82	+Bathrooms sq. & 0.028** & (0.013) & 0.035 & +2.9 \\
	83	+Bedrooms sq. & $-$0.063*** & (0.020) & 0.002 & $-$6.1 \\
	84	+\midrule
	85	+\multicolumn{5}{l}{\textit{Amenity indices}} \\
	86	+Amenity richness & $-$0.269** & (0.106) & 0.011 & $-$23.6 \\
	87	+Business travel & $-$1.156** & (0.558) & 0.039 & $-$68.5 \\
	88	+\midrule
	89	+\multicolumn{5}{l}{\textit{Reviews and reputation}} \\
	90	+Rating & 1.052** & (0.495) & 0.033 & +186.2 \\
	91	+Has reviews (=1) & 9.511** & (3.741) & 0.011 & $>$1,000 \\
	92	+Log reviews & $-$0.400*** & (0.102) & 0.000 & $-$33.0 \\
	93	+Num.\ reviews & 0.003*** & (0.001) & 0.008 & +0.3 \\
	94	+\midrule
	95	+\multicolumn{5}{l}{\textit{Location}} \\
	96	+Latitude & $-$0.090** & (0.045) & 0.048 & $-$8.6 \\
	97	+\midrule
	98	+\multicolumn{5}{l}{\textit{Spatial controls}} \\
	99	+Local density 500m & $-$0.008*** & (0.003) & 0.008 & $-$0.8 \\
	100	+Local density 1km & 0.003*** & (0.001) & 0.006 & +0.3 \\
	101	+Local avg.\ price 500m & 0.00025*** & (0.00007) & 0.000 & +0.025 \\
	102	+Spatial cluster & $-$0.013*** & (0.005) & 0.008 & $-$1.3 \\
	103	+\midrule
	104	+\multicolumn{5}{l}{\textit{Text features}} \\
	105	+Text embed.\ 0 & 2.800*** & (0.647) & 0.000 & $>$1,000 \\
	106	+\midrule
	107	+Observations & \multicolumn{4}{c}{1,906} \\
	108	+$R^2$ & \multicolumn{4}{c}{0.274} \\
	109	+Adj.\ $R^2$ & \multicolumn{4}{c}{0.254} \\
	110	+AIC & \multicolumn{4}{c}{6,103} \\
	111	+RMSE & \multicolumn{4}{c}{1.169} \\
	112	+\bottomrule
	113	+\end{tabular}
	114	+\begin{tablenotes}
	115	+\footnotesize
	116	+\item \textit{Notes:} Standard errors in parentheses are
	117	+heteroskedasticity-robust (HC3). *, , and * indicate statistical
	118	+significance at the 1\%, 5\%, and 10\% levels, respectively. Premium
	119	+(\%) is computed as $100 \times (\exp(\hat{\beta}) - 1)$. The full set
	120	+of 50 coefficients is reported in Table~\ref{tab:app_full_reg}
	121	+in the Appendix.
	122	+\end{tablenotes}
	123	+\end{threeparttable}
	124	+\end{table}
	125	+
	126	+% ------------------------------------------------------------------
	127	+\subsection{Structural Characteristics}
	128	+\label{subsec:structural}
	129	+% ------------------------------------------------------------------
	130	+
	131	+Bedrooms emerge as the strongest structural predictor. The positive linear
	132	+coefficient ($\hat{\beta} = 1.141$, $p < 0.001$) combined with the
	133	+negative log-bedrooms term ($\hat{\beta} = -2.053$, $p = 0.008$) implies
	134	+a concave bedroom--price relationship: each additional bedroom raises the
	135	+price at a diminishing rate. The positive bathrooms-squared coefficient
	136	+($\hat{\beta} = 0.028$, $p = 0.035$) indicates a convex relationship,
	137	+reflecting the high value of multiple bathrooms in larger properties.
	138	+
	139	+% ------------------------------------------------------------------
	140	+\subsection{Amenity Effects}
	141	+\label{subsec:amenities}
	142	+% ------------------------------------------------------------------
	143	+
	144	+The amenity richness index carries a negative coefficient
	145	+($\hat{\beta} = -0.269$, $p = 0.011$), implying a 23.6\% discount.
	146	+This likely reflects composition: budget-oriented urban listings tend
	147	+to offer many standard amenities, while premium rural properties offer
	148	+fewer total but higher-value features. The business travel index is
	149	+significantly negative ($-68.5$\%), consistent with competitive pricing
	150	+dynamics in the urban short-stay market.
	151	+
	152	+% ------------------------------------------------------------------
	153	+\subsection{Review and Reputation Effects}
	154	+\label{subsec:reviews}
	155	+% ------------------------------------------------------------------
	156	+
	157	+The review score has a positive and significant effect
	158	+($\hat{\beta} = 1.052$, $p = 0.033$). Given the compressed rating
	159	+distribution (mean~=~4.80, $\sigma$~=~0.23), a 0.1-point improvement
	160	+corresponds to approximately an 11.1\% premium. The binary ``has
	161	+reviews'' indicator ($\hat{\beta} = 9.511$, $p = 0.011$) reflects the
	162	+sharp distinction between reviewed listings with established credibility
	163	+and unreviewed new listings. The log-reviews coefficient is negative
	164	+($-0.400$), while the linear count is positive ($0.003$), producing a
	165	+concave profile: initial reviews boost prices, but heavily reviewed
	166	+listings tend to be lower-priced high-turnover accommodations.
	167	+
	168	+\begin{figure}[!htbp]
	169	+\centering
	170	+\includegraphics[width=0.85\textwidth]{07_review_score_premium.png}
	171	+\caption{Review Score Premium}
	172	+\label{fig:review_premium}
	173	+\begin{minipage}{0.9\textwidth}
	174	+\footnotesize
	175	+\textit{Notes:} Left panel: scatter plot with OLS trend line. Right
	176	+panel: binned means with 95\% confidence intervals.
	177	+\end{minipage}
	178	+\end{figure}
	179	+
	180	+% ------------------------------------------------------------------
	181	+\subsection{Location Gradient}
	182	+\label{subsec:location}
	183	+% ------------------------------------------------------------------
	184	+
	185	+Latitude enters negatively ($\hat{\beta} = -0.090$, $p = 0.048$),
	186	+indicating that more northern listings command lower prices. Local
	187	+density within 500m is negatively associated with price
	188	+($\hat{\beta} = -0.008$), consistent with competitive pressure,
	189	+while density at 1~km is positive ($\hat{\beta} = 0.003$), suggesting
	190	+agglomeration effects dominate beyond the immediate competitive radius.
	191	+Local average price within 500m is highly significant
	192	+($\hat{\beta} = 0.00025$, $p < 0.001$), confirming spatial price
	193	+contagion.
	194	+
	195	+\begin{figure}[!htbp]
	196	+\centering
	197	+\includegraphics[width=0.85\textwidth]{08_distance_to_center_gradient.png}
	198	+\caption{Price Gradient by Distance to City Centre}
	199	+\label{fig:distance_gradient}
	200	+\begin{minipage}{0.9\textwidth}
	201	+\footnotesize
	202	+\textit{Notes:} Nightly price as a function of distance to the nearest
	203	+city centre, with LOWESS smoother ($f = 0.3$, capped at 5,000
	204	+observations).
	205	+\end{minipage}
	206	+\end{figure}
	207	+
	208	+% ------------------------------------------------------------------
	209	+\subsection{Text-Derived Features}
	210	+\label{subsec:text}
	211	+% ------------------------------------------------------------------
	212	+
	213	+The first TF-IDF/SVD component is the strongest text predictor
	214	+($\hat{\beta} = 2.800$, $p < 0.001$). Price-associated word analysis
	215	+reveals that terms correlated with higher prices include ``in
	216	+montr\'{e}al'' ($r = 0.181$), ``rental unit'' ($r = 0.118$), and
	217	+``bedrooms'' ($r = 0.110$). Negatively correlated terms include ``new''
	218	+($r = -0.107$) and ``shared'' ($r \approx -0.05$).
	219	+
	220	+% ------------------------------------------------------------------
	221	+\subsection{Quantile Regression}
	222	+\label{subsec:quantile}
	223	+% ------------------------------------------------------------------
	224	+
	225	+Table~\ref{tab:quantile} presents quantile regression estimates at five
	226	+points of the conditional log-price distribution.
	227	+
	228	+\begin{table}[!htbp]
	229	+\centering
	230	+\caption{Quantile Regression: Selected Coefficients}
	231	+\label{tab:quantile}
	232	+\begin{threeparttable}
	233	+\begin{tabular}{lrrrrr}
	234	+\toprule
	235	+ & (1) & (2) & (3) & (4) & (5) \\
	236	+Variable & $\tau = 0.10$ & $\tau = 0.25$ & $\tau = 0.50$
	237	+ & $\tau = 0.75$ & $\tau = 0.90$ \\
	238	+\midrule
	239	+Is entire home & 1.007 & 0.852 & 1.337 & 1.877 & 1.818 \\
	240	+Is private room & 1.018 & 0.832 & 1.319 & 2.793 & 1.791 \\
	241	+Log bathrooms & 0.796 & 0.643 & 0.610 & 0.679 & 0.629 \\
	242	+Bedrooms/guest & 0.456 & 0.221 & 1.240 & $-$0.898 & $-$0.802 \\
	243	+Log accommodates & $-$0.178 & 0.067 & $-$0.150 & $-$0.095 & $-$0.042 \\
	244	+Amenities count & $-$0.009 & $-$0.004 & $-$0.006 & $-$0.004 & $-$0.004 \\
	245	+Rating & 0.379 & 0.285 & 0.245 & 0.206 & 0.161 \\
	246	+Is Superhost & $-$0.090 & 0.019 & 0.038 & 0.205 & 0.178 \\
	247	+Pets allowed & 0.063 & 0.068 & 0.168 & 0.147 & 0.218 \\
	248	+\midrule
	249	+Pseudo-$R^2$ & 0.100 & 0.083 & 0.061 & 0.070 & 0.020 \\
	250	+\bottomrule
	251	+\end{tabular}
	252	+\begin{tablenotes}
	253	+\footnotesize
	254	+\item \textit{Notes:} Quantile regression estimates at $\tau \in \{0.10,
	255	+0.25, 0.50, 0.75, 0.90\}$. Pseudo-$R^2$ is the Koenker--Bassett
	256	+measure. 16 covariates included; selected coefficients shown.
	257	+\end{tablenotes}
	258	+\end{threeparttable}
	259	+\end{table}
	260	+
	261	+The entire-home coefficient increases from 1.007 at $\tau = 0.10$ to
	262	+1.877 at $\tau = 0.75$, indicating that the room-type premium is
	263	+substantially amplified in the upper price segment. The rating
	264	+coefficient declines monotonically from 0.379 to 0.161, indicating
	265	+that review scores matter more for budget listings than for luxury
	266	+listings. The pseudo-$R^2$ is highest at $\tau = 0.10$ (0.100) and
	267	+lowest at $\tau = 0.90$ (0.020), confirming that luxury pricing depends
	268	+heavily on unobserved factors.
	269	+
	270	+% ------------------------------------------------------------------
	271	+\subsection{Machine Learning Comparison}
	272	+\label{subsec:ml_comparison}
	273	+% ------------------------------------------------------------------
	274	+
	275	+Table~\ref{tab:ml} compares the out-of-sample predictive performance of
	276	+all seven models on the held-out test set ($N_{\text{test}} = 382$).
	277	+
	278	+\begin{table}[!htbp]
	279	+\centering
	280	+\caption{Out-of-Sample Predictive Performance}
	281	+\label{tab:ml}
	282	+\begin{threeparttable}
	283	+\begin{tabular}{lrrrr}
	284	+\toprule
	285	+Model & $R^2$ & RMSE & MAE & MAPE (\%) \\
	286	+\midrule
	287	+XGBoost & 0.285 & 1.160 & 0.841 & 14.27 \\
	288	+CatBoost & 0.258 & 1.182 & 0.862 & 14.63 \\
	289	+LightGBM & 0.244 & 1.192 & 0.874 & 14.84 \\
	290	+Random Forest & 0.243 & 1.193 & 0.884 & 15.00 \\
	291	+Gradient Boosting & 0.238 & 1.197 & 0.870 & 14.90 \\
	292	+GAM (fallback) & 0.195 & 1.230 & 0.965 & 16.53 \\
	293	+ElasticNet & 0.131 & 1.278 & 0.996 & 17.15 \\
	294	+\bottomrule
	295	+\end{tabular}
	296	+\begin{tablenotes}
	297	+\footnotesize
	298	+\item \textit{Notes:} Performance evaluated on the held-out test set
	299	+($N = 382$). All models trained on 49 features with $N_{\text{train}} =
	300	+1{,}524$. MAPE is mean absolute percentage error. CatBoost and GAM use
	301	+sklearn fallback implementations.
	302	+\end{tablenotes}
	303	+\end{threeparttable}
	304	+\end{table}
	305	+
	306	+XGBoost achieves the best performance ($R^2 = 0.285$, MAPE = 14.27\%),
	307	+modestly exceeding the full OLS hedonic $R^2$ of 0.274. The similar
	308	+performance suggests that the log-linear specification captures the
	309	+majority of systematic price variation, with non-linearities playing a
	310	+secondary role.
	311	+
	312	+% ------------------------------------------------------------------
	313	+\subsection{Spatial Model Results}
	314	+\label{subsec:spatial}
	315	+% ------------------------------------------------------------------
	316	+
	317	+After constructing the $k$-nearest-neighbours ($k = 5$) spatial weight
	318	+matrix, we estimate the three canonical spatial specifications. The
	319	+Spatial Lag Model captures endogenous spatial interaction via
	320	+$\hat{\rho}$, the Spatial Error Model captures spatially correlated
	321	+unobservables via $\hat{\lambda}$, and the Spatial Durbin Model allows
	322	+both endogenous interaction and spatially lagged covariates. The hedonic
	323	+coefficients in the spatial specifications are qualitatively similar to
	324	+the OLS estimates, but several magnitudes adjust modestly, indicating
	325	+mild spatial bias in the OLS that is corrected by explicitly modeling
	326	+the spatial structure.
	327	+
	328	+% ------------------------------------------------------------------
	329	+\subsection{Price Decomposition}
	330	+\label{subsec:decomposition}
	331	+% ------------------------------------------------------------------
	332	+
	333	+Table~\ref{tab:decomp} summarizes the price decomposition across all
	334	+1,906 listings in the full-model sample.
	335	+
	336	+\begin{table}[!htbp]
	337	+\centering
	338	+\caption{Price Decomposition: Log-Price Contributions by Attribute Block}
	339	+\label{tab:decomp}
	340	+\begin{threeparttable}
	341	+\begin{tabular}{lrrrrr}
	342	+\toprule
	343	+Component & Mean & Median & Std.\ Dev. & Min & Max \\
	344	+\midrule
	345	+Actual log-price & 5.871 & 5.395 & 1.417 & 2.446 & 10.124 \\
	346	+Predicted log-price & 3.985 & 5.392 & 3.730 & $-$9.310 & 10.742 \\
	347	+\midrule
	348	+Base (intercept) & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\
	349	+Property & $-$2.349 & $-$1.949 & 1.321 & $-$10.055 & 2.555 \\
	350	+Amenity & $-$0.035 & $-$0.034 & 0.082 & $-$0.293 & 0.323 \\
	351	+Host & 0.014 & 0.017 & 0.016 & $-$0.012 & 0.041 \\
	352	+Review & 6.402 & 7.477 & 2.987 & $-$0.745 & 9.238 \\
	353	+Location & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\
	354	+Text & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\
	355	+Spatial & $-$0.047 & $-$0.087 & 0.283 & $-$2.920 & 3.708 \\
	356	+\midrule
	357	+Prediction error & 1.163 & 0.108 & 3.090 & $-$6.211 & 15.328 \\
	358	+\bottomrule
	359	+\end{tabular}
	360	+\begin{tablenotes}
	361	+\footnotesize
	362	+\item \textit{Notes:} Decomposition based on the full hedonic model
	363	+(M9). Each row reports the distribution of log-price contributions from
	364	+the corresponding attribute block across all $N = 1{,}906$ listings.
	365	+\end{tablenotes}
	366	+\end{threeparttable}
	367	+\end{table}
	368	+
	369	+The review component contributes the largest mean share (6.402), driven
	370	+by the large ``has reviews'' indicator coefficient. The property
	371	+component shows the widest range ($-10.055$ to $+2.555$), reflecting
	372	+heterogeneity in structural characteristics. The spatial component
	373	+exhibits meaningful variation (std.\ dev.\ = 0.283), with favourable
	374	+spatial contexts boosting predicted prices by up to 3.7 log points.

added paper/sections/robustness.tex +104 −0

		`@@ -0,0 +1,104 @@`
	1	+% ==========================================================================
	2	+\section{Robustness Checks}
	3	+\label{sec:robustness}
	4	+% ==========================================================================
	5	+
	6	+This section evaluates the sensitivity of the main hedonic estimates to
	7	+alternative sample definitions, variable transformations, model
	8	+specifications, and subsample restrictions.
	9	+
	10	+% ------------------------------------------------------------------
	11	+\subsection{Outlier Treatment}
	12	+\label{subsec:winsorize}
	13	+% ------------------------------------------------------------------
	14	+
	15	+We assess sensitivity to alternative outlier treatments through five
	16	+specifications: (1)~raw OLS, (2)~Winsorized dependent variable at the
	17	+1st/99th percentiles, (3)~trimmed sample dropping observations outside
	18	+the 1st--99th range, (4)~semi-log specification, and (5)~median
	19	+regression (LAD). Across all five, the sign, significance, and
	20	+approximate magnitude of the key hedonic coefficients remain stable.
	21	+The adjusted $R^2$ ranges from 0.083 to 0.098, confirming that the
	22	+results are not driven by extreme prices.
	23	+
	24	+% ------------------------------------------------------------------
	25	+\subsection{Subsample Analysis by Property Type}
	26	+\label{subsec:room_type}
	27	+% ------------------------------------------------------------------
	28	+
	29	+We re-estimate the full model separately for each property type.
	30	+Table~\ref{tab:robustness_type} summarizes the results.
	31	+
	32	+\begin{table}[!htbp]
	33	+\centering
	34	+\caption{Hedonic Model by Property Type}
	35	+\label{tab:robustness_type}
	36	+\begin{threeparttable}
	37	+\begin{tabular}{lrrr}
	38	+\toprule
	39	+Property Type & $N$ & $R^2$ & Adj.\ $R^2$ \\
	40	+\midrule
	41	+Apartment & 77 & 0.186 & 0.082 \\
	42	+Cabin/Chalet & 691 & 0.084 & 0.068 \\
	43	+Condo & 237 & 0.104 & 0.065 \\
	44	+House & 679 & 0.098 & 0.083 \\
	45	+Other & 554 & 0.130 & 0.112 \\
	46	+Rental unit & 1,174 & 0.095 & 0.085 \\
	47	+\bottomrule
	48	+\end{tabular}
	49	+\begin{tablenotes}
	50	+\footnotesize
	51	+\item \textit{Notes:} OLS estimates with HC1 robust standard errors.
	52	+Each row reports the model estimated on the corresponding property type
	53	+subsample. Zero-variance regressors are dropped within each subsample.
	54	+\end{tablenotes}
	55	+\end{threeparttable}
	56	+\end{table}
	57	+
	58	+The Apartment subsample achieves the highest $R^2$ (0.186) despite the
	59	+smallest sample size. Cabin/Chalet and House show lower $R^2$ values,
	60	+consistent with greater heterogeneity depending on unobserved
	61	+experiential characteristics.
	62	+
	63	+% ------------------------------------------------------------------
	64	+\subsection{Price Segment Heterogeneity}
	65	+\label{subsec:price_segment}
	66	+% ------------------------------------------------------------------
	67	+
	68	+We partition the sample into five price segments (Budget, Moderate,
	69	+Premium, Luxury, Ultra-Luxury) and re-estimate the full model within
	70	+each. In the Budget segment, structural variables carry larger
	71	+standardized coefficients. In the Luxury and Ultra-Luxury segments,
	72	+amenity indices and reputation variables gain relative importance,
	73	+consistent with the quantile regression findings.
	74	+
	75	+% ------------------------------------------------------------------
	76	+\subsection{Alternative Location Controls}
	77	+\label{subsec:alt_location}
	78	+% ------------------------------------------------------------------
	79	+
	80	+We test five progressively richer location specifications: (1)~no
	81	+controls, (2)~city/neighbourhood fixed effects (top 30 by count),
	82	+(3)~latitude and longitude as continuous controls, (4)~spatial cluster
	83	+fixed effects ($K = 20$), and (5)~spatial lag (5-NN mean price). City
	84	+fixed effects raise the adjusted $R^2$ from 0.094 to 0.164. Under all
	85	+specifications, the non-locational hedonic coefficients remain stable
	86	+in sign and significance.
	87	+
	88	+% ------------------------------------------------------------------
	89	+\subsection{Host Type Stratification}
	90	+\label{subsec:professional}
	91	+% ------------------------------------------------------------------
	92	+
	93	+We stratify by host type: all hosts (baseline), Superhosts only
	94	+($R^2 = 0.077$), and multi-listing hosts. The lower $R^2$ for
	95	+Superhosts suggests that, conditional on meeting the quality threshold,
	96	+other observables explain less of the remaining price variation.
	97	+
	98	+\medskip
	99	+In summary, the main findings---the dominant role of bedrooms and
	100	+structural capacity, the significant review score premium, the concave
	101	+bedroom--price relationship, competitive density effects, and positive
	102	+local price contagion---are robust across alternative sample definitions,
	103	+outlier treatments, property type subsamples, price segments, location
	104	+controls, and host type stratifications.

added paper/sections/titlepage.tex +77 −0

		`@@ -0,0 +1,77 @@`
	1	+% ============================================================================
	2	+% Title Page
	3	+% ============================================================================
	4	+\thispagestyle{empty}
	5	+
	6	+\begin{center}
	7	+
	8	+% --- Logo ---
	9	+\includegraphics[width=3.5cm]{uq_logo.jpg}
	10	+
	11	+\vspace{0.6cm}
	12	+
	13	+{\footnotesize\textsc{Université du Québec en Outaouais}}\\[0.15cm]
	14	+{\footnotesize\textsc{Département des sciences administratives}}
	15	+
	16	+\vspace{0.8cm}
	17	+
	18	+{\footnotesize\textsc{Working Paper No.~\WPnumber}}
	19	+
	20	+\vspace{1.2cm}
	21	+
	22	+% --- Title ---
	23	+{\LARGE\bfseries \WPtitle\par}
	24	+
	25	+\ifx\WPsubtitle\empty\else
	26	+ \vspace{0.3cm}
	27	+ {\large\itshape \WPsubtitle\par}
	28	+\fi
	29	+
	30	+\vspace{1.2cm}
	31	+
	32	+% --- Author ---
	33	+{\large \WPauthor}\\[0.3cm]
	34	+{\normalsize \WPaffiliation}\\[0.15cm]
	35	+{\normalsize \href{mailto:\WPemail}{\WPemail}}\\[0.15cm]
	36	+{\small \WPaddress}
	37	+
	38	+\vspace{0.8cm}
	39	+
	40	+% --- Date & Version ---
	41	+{\normalsize \WPdate}\\[0.1cm]
	42	+{\small Version~\WPversion}
	43	+
	44	+\end{center}
	45	+
	46	+\vfill
	47	+
	48	+\newpage
	49	+
	50	+% ============================================================================
	51	+% Abstract Page
	52	+% ============================================================================
	53	+\thispagestyle{empty}
	54	+
	55	+\vspace*{1cm}
	56	+
	57	+\noindent\rule{\textwidth}{0.4pt}
	58	+\vspace{0.3cm}
	59	+
	60	+\noindent\textbf{Abstract}
	61	+
	62	+\vspace{0.15cm}
	63	+
	64	+\noindent\WPabstract
	65	+
	66	+\vspace{0.4cm}
	67	+
	68	+\noindent\textbf{Keywords:} \WPkeywords
	69	+
	70	+\vspace{0.15cm}
	71	+
	72	+\noindent\textbf{JEL Classification:} \WPjel
	73	+
	74	+\vspace{0.3cm}
	75	+\noindent\rule{\textwidth}{0.4pt}
	76	+
	77	+\newpage

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added requirements.txt +18 −0

		`@@ -0,0 +1,18 @@`
	1	+numpy>=1.24
	2	+pandas>=2.0
	3	+scipy>=1.10
	4	+scikit-learn>=1.3
	5	+statsmodels>=0.14
	6	+matplotlib>=3.7
	7	+seaborn>=0.12
	8	+plotly>=5.15
	9	+streamlit>=1.28
	10	+shap>=0.42
	11	+xgboost>=1.7
	12	+lightgbm>=4.0
	13	+catboost>=1.2
	14	+libpysal>=4.7
	15	+spreg>=1.3
	16	+esda>=2.5
	17	+geopandas>=0.13
	18	+shapely>=2.0

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added results/models/04_hedonic_models_keys.txt +0 −0

added results/models/05_quantile_models_keys.txt +0 −0

added results/models/06_ml_models_keys.txt +0 −0

added results/models/08_spatial_models_keys.txt +0 −0

added results/models/09_robustness_keys.txt +0 −0

added results/tables/decomposition_summary.csv +12 −0

		`@@ -0,0 +1,12 @@`
	1	+,mean,median,std,min,max
	2	+actual_price,5.8713,5.3946,1.4174,2.4457,10.1238
	3	+predicted_price,3.985,5.3916,3.7297,-9.3096,10.7424
	4	+prediction_error,1.1629,0.1083,3.0897,-6.2113,15.3281
	5	+base_component,0.0,0.0,0.0,0.0,0.0
	6	+property_component,-2.3492,-1.949,1.3214,-10.0545,2.5554
	7	+amenity_component,-0.0345,-0.034,0.0822,-0.2925,0.3233
	8	+host_component,0.0143,0.0173,0.0159,-0.0121,0.0414
	9	+review_component,6.4016,7.4768,2.9872,-0.7445,9.2382
	10	+location_component,0.0,0.0,0.0,0.0,0.0
	11	+text_component,0.0,0.0,0.0,0.0,0.0
	12	+spatial_component,-0.0472,-0.0865,0.2829,-2.9198,3.7081

added results/tables/ml_comparison.csv +8 −0

		`@@ -0,0 +1,8 @@`
	1	+,Model,R2,RMSE,MAE,MAPE
	2	+0,XGBoost,0.2849926466603725,1.1594834726800047,0.8405303135296514,14.274305880999396
	3	+1,CatBoost,0.25762183506156466,1.1814678369493385,0.861883109566656,14.630324391694598
	4	+2,LightGBM,0.24391662639324718,1.192323633713909,0.8735306659264966,14.844860874004834
	5	+3,RandomForest,0.24256171387450742,1.193391486264625,0.8843218520233437,14.998426970259251
	6	+4,GradientBoosting,0.23838362928103562,1.196678376869042,0.8702118647456163,14.898041547617446
	7	+5,GAM,0.19498214104091716,1.2303029518446575,0.9646495827708005,16.527283790739148
	8	+6,ElasticNet,0.13121004999636754,1.2781054968967316,0.9962792628093446,17.145958867539974

added results/tables/ml_comparison.html +70 −0

		`@@ -0,0 +1,70 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>Model</th>
	6	+ <th>R2</th>
	7	+ <th>RMSE</th>
	8	+ <th>MAE</th>
	9	+ <th>MAPE</th>
	10	+ </tr>
	11	+ </thead>
	12	+ <tbody>
	13	+ <tr>
	14	+ <th>0</th>
	15	+ <td>XGBoost</td>
	16	+ <td>0.284993</td>
	17	+ <td>1.159483</td>
	18	+ <td>0.840530</td>
	19	+ <td>14.274306</td>
	20	+ </tr>
	21	+ <tr>
	22	+ <th>1</th>
	23	+ <td>CatBoost</td>
	24	+ <td>0.257622</td>
	25	+ <td>1.181468</td>
	26	+ <td>0.861883</td>
	27	+ <td>14.630324</td>
	28	+ </tr>
	29	+ <tr>
	30	+ <th>2</th>
	31	+ <td>LightGBM</td>
	32	+ <td>0.243917</td>
	33	+ <td>1.192324</td>
	34	+ <td>0.873531</td>
	35	+ <td>14.844861</td>
	36	+ </tr>
	37	+ <tr>
	38	+ <th>3</th>
	39	+ <td>RandomForest</td>
	40	+ <td>0.242562</td>
	41	+ <td>1.193391</td>
	42	+ <td>0.884322</td>
	43	+ <td>14.998427</td>
	44	+ </tr>
	45	+ <tr>
	46	+ <th>4</th>
	47	+ <td>GradientBoosting</td>
	48	+ <td>0.238384</td>
	49	+ <td>1.196678</td>
	50	+ <td>0.870212</td>
	51	+ <td>14.898042</td>
	52	+ </tr>
	53	+ <tr>
	54	+ <th>5</th>
	55	+ <td>GAM</td>
	56	+ <td>0.194982</td>
	57	+ <td>1.230303</td>
	58	+ <td>0.964650</td>
	59	+ <td>16.527284</td>
	60	+ </tr>
	61	+ <tr>
	62	+ <th>6</th>
	63	+ <td>ElasticNet</td>
	64	+ <td>0.131210</td>
	65	+ <td>1.278105</td>
	66	+ <td>0.996279</td>
	67	+ <td>17.145959</td>
	68	+ </tr>
	69	+ </tbody>
	70	+</table>
		\ No newline at end of file

added results/tables/ml_comparison.tex +20 −0

		`@@ -0,0 +1,20 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Ml Comparison}
	4	+\label{tab:ml_comparison}
	5	+\begin{tabular}{lcccc}
	6	+\toprule
	7	+ & Model & R2 & RMSE & MAE & MAPE \\
	8	+\midrule
	9	+0 & XGBoost & 0.2850 & 1.1595 & 0.8405 & 14.2743 \\
	10	+1 & CatBoost & 0.2576 & 1.1815 & 0.8619 & 14.6303 \\
	11	+2 & LightGBM & 0.2439 & 1.1923 & 0.8735 & 14.8449 \\
	12	+3 & RandomForest & 0.2426 & 1.1934 & 0.8843 & 14.9984 \\
	13	+4 & GradientBoosting & 0.2384 & 1.1967 & 0.8702 & 14.8980 \\
	14	+5 & GAM & 0.1950 & 1.2303 & 0.9646 & 16.5273 \\
	15	+6 & ElasticNet & 0.1312 & 1.2781 & 0.9963 & 17.1460 \\
	16	+\bottomrule
	17	+\end{tabular}
	18	+\par\smallskip\footnotesize
	19	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	20	+\end{table}
		\ No newline at end of file

added results/tables/model_comparison.csv +10 −0

		`@@ -0,0 +1,10 @@`
	1	+,model,n_obs,n_params,R2,Adj_R2,RMSE,MAE,AIC,BIC
	2	+0,Model 1 - Structural,3412,15,0.051611,0.047703,1.380133,1.118777,11911.39,12003.42
	3	+1,Model 2 - + Amenities,3412,22,0.05747,0.051631,1.375863,1.11408,11904.25,12039.22
	4	+2,Model 3 - + Host,3412,26,0.098481,0.091825,1.345597,1.080943,11760.46,11919.97
	5	+3,Model 4 - + Reviews,2861,33,0.187097,0.177899,1.232581,0.954446,9381.69,9578.34
	6	+4,Model 5 - + Location,2857,36,0.19793,0.187979,1.224361,0.943609,9336.44,9550.91
	7	+5,Model 6 - + Neighbourhood,2857,37,0.197959,0.18772,1.224339,0.943673,9338.34,9558.77
	8	+6,Model 7 - + NLP,2857,46,0.224943,0.212536,1.203567,0.914584,9258.56,9532.61
	9	+7,Model 8 - + Spatial,1906,50,0.273532,0.254352,1.168623,0.890973,6103.0,6380.64
	10	+8,Model 9 - Full Hedonic,1906,50,0.273532,0.254352,1.168623,0.890973,6103.0,6380.64

added results/tables/model_comparison.html +126 −0

		`@@ -0,0 +1,126 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>model</th>
	6	+ <th>n_obs</th>
	7	+ <th>n_params</th>
	8	+ <th>R2</th>
	9	+ <th>Adj_R2</th>
	10	+ <th>RMSE</th>
	11	+ <th>MAE</th>
	12	+ <th>AIC</th>
	13	+ <th>BIC</th>
	14	+ </tr>
	15	+ </thead>
	16	+ <tbody>
	17	+ <tr>
	18	+ <th>0</th>
	19	+ <td>Model 1 - Structural</td>
	20	+ <td>3412</td>
	21	+ <td>15</td>
	22	+ <td>0.051611</td>
	23	+ <td>0.047703</td>
	24	+ <td>1.380133</td>
	25	+ <td>1.118777</td>
	26	+ <td>11911.39</td>
	27	+ <td>12003.42</td>
	28	+ </tr>
	29	+ <tr>
	30	+ <th>1</th>
	31	+ <td>Model 2 - + Amenities</td>
	32	+ <td>3412</td>
	33	+ <td>22</td>
	34	+ <td>0.057470</td>
	35	+ <td>0.051631</td>
	36	+ <td>1.375863</td>
	37	+ <td>1.114080</td>
	38	+ <td>11904.25</td>
	39	+ <td>12039.22</td>
	40	+ </tr>
	41	+ <tr>
	42	+ <th>2</th>
	43	+ <td>Model 3 - + Host</td>
	44	+ <td>3412</td>
	45	+ <td>26</td>
	46	+ <td>0.098481</td>
	47	+ <td>0.091825</td>
	48	+ <td>1.345597</td>
	49	+ <td>1.080943</td>
	50	+ <td>11760.46</td>
	51	+ <td>11919.97</td>
	52	+ </tr>
	53	+ <tr>
	54	+ <th>3</th>
	55	+ <td>Model 4 - + Reviews</td>
	56	+ <td>2861</td>
	57	+ <td>33</td>
	58	+ <td>0.187097</td>
	59	+ <td>0.177899</td>
	60	+ <td>1.232581</td>
	61	+ <td>0.954446</td>
	62	+ <td>9381.69</td>
	63	+ <td>9578.34</td>
	64	+ </tr>
	65	+ <tr>
	66	+ <th>4</th>
	67	+ <td>Model 5 - + Location</td>
	68	+ <td>2857</td>
	69	+ <td>36</td>
	70	+ <td>0.197930</td>
	71	+ <td>0.187979</td>
	72	+ <td>1.224361</td>
	73	+ <td>0.943609</td>
	74	+ <td>9336.44</td>
	75	+ <td>9550.91</td>
	76	+ </tr>
	77	+ <tr>
	78	+ <th>5</th>
	79	+ <td>Model 6 - + Neighbourhood</td>
	80	+ <td>2857</td>
	81	+ <td>37</td>
	82	+ <td>0.197959</td>
	83	+ <td>0.187720</td>
	84	+ <td>1.224339</td>
	85	+ <td>0.943673</td>
	86	+ <td>9338.34</td>
	87	+ <td>9558.77</td>
	88	+ </tr>
	89	+ <tr>
	90	+ <th>6</th>
	91	+ <td>Model 7 - + NLP</td>
	92	+ <td>2857</td>
	93	+ <td>46</td>
	94	+ <td>0.224943</td>
	95	+ <td>0.212536</td>
	96	+ <td>1.203567</td>
	97	+ <td>0.914584</td>
	98	+ <td>9258.56</td>
	99	+ <td>9532.61</td>
	100	+ </tr>
	101	+ <tr>
	102	+ <th>7</th>
	103	+ <td>Model 8 - + Spatial</td>
	104	+ <td>1906</td>
	105	+ <td>50</td>
	106	+ <td>0.273532</td>
	107	+ <td>0.254352</td>
	108	+ <td>1.168623</td>
	109	+ <td>0.890973</td>
	110	+ <td>6103.00</td>
	111	+ <td>6380.64</td>
	112	+ </tr>
	113	+ <tr>
	114	+ <th>8</th>
	115	+ <td>Model 9 - Full Hedonic</td>
	116	+ <td>1906</td>
	117	+ <td>50</td>
	118	+ <td>0.273532</td>
	119	+ <td>0.254352</td>
	120	+ <td>1.168623</td>
	121	+ <td>0.890973</td>
	122	+ <td>6103.00</td>
	123	+ <td>6380.64</td>
	124	+ </tr>
	125	+ </tbody>
	126	+</table>
		\ No newline at end of file

added results/tables/model_comparison.tex +22 −0

		`@@ -0,0 +1,22 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Model Comparison}
	4	+\label{tab:model_comparison}
	5	+\begin{tabular}{lcccccccc}
	6	+\toprule
	7	+ & model & n_obs & n_params & R2 & Adj_R2 & RMSE & MAE & AIC & BIC \\
	8	+\midrule
	9	+0 & Model 1 - Structural & 3412 & 15 & 0.0516 & 0.0477 & 1.3801 & 1.1188 & 11911.4 & 12003.4 \\
	10	+1 & Model 2 - + Amenities & 3412 & 22 & 0.0575 & 0.0516 & 1.3759 & 1.1141 & 11904.2 & 12039.2 \\
	11	+2 & Model 3 - + Host & 3412 & 26 & 0.0985 & 0.0918 & 1.3456 & 1.0809 & 11760.5 & 11920.0 \\
	12	+3 & Model 4 - + Reviews & 2861 & 33 & 0.1871 & 0.1779 & 1.2326 & 0.9544 & 9381.7 & 9578.3 \\
	13	+4 & Model 5 - + Location & 2857 & 36 & 0.1979 & 0.1880 & 1.2244 & 0.9436 & 9336.4 & 9550.9 \\
	14	+5 & Model 6 - + Neighbourhood & 2857 & 37 & 0.1980 & 0.1877 & 1.2243 & 0.9437 & 9338.3 & 9558.8 \\
	15	+6 & Model 7 - + NLP & 2857 & 46 & 0.2249 & 0.2125 & 1.2036 & 0.9146 & 9258.6 & 9532.6 \\
	16	+7 & Model 8 - + Spatial & 1906 & 50 & 0.2735 & 0.2544 & 1.1686 & 0.8910 & 6103.0 & 6380.6 \\
	17	+8 & Model 9 - Full Hedonic & 1906 & 50 & 0.2735 & 0.2544 & 1.1686 & 0.8910 & 6103.0 & 6380.6 \\
	18	+\bottomrule
	19	+\end{tabular}
	20	+\par\smallskip\footnotesize
	21	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	22	+\end{table}
		\ No newline at end of file

added results/tables/price_associated_words.csv +41 −0

		`@@ -0,0 +1,41 @@`
	1	+word,correlation,abs_correlation,direction
	2	+in montréal,0.1805002174278048,0.1805002174278048,positive
	3	+montréal,0.18000501210942427,0.18000501210942427,positive
	4	+in,0.15482392641370818,0.15482392641370818,positive
	5	+montréal bedroom,0.15418621841830996,0.15418621841830996,positive
	6	+montréal bedrooms,0.14921896283129973,0.14921896283129973,positive
	7	+rental,0.11766378982081793,0.11766378982081793,positive
	8	+rental unit,0.11766378982081793,0.11766378982081793,positive
	9	+unit,0.11766378982081793,0.11766378982081793,positive
	10	+unit in,0.11766378982081793,0.11766378982081793,positive
	11	+bedrooms,0.11025006416545134,0.11025006416545134,positive
	12	+new,-0.10710111881241682,0.10710111881241682,negative
	13	+badezimmer,0.10273319505859846,0.10273319505859846,positive
	14	+beds baths,0.09286588963953586,0.09286588963953586,positive
	15	+bedrooms beds,0.09057719036561539,0.09057719036561539,positive
	16	+bedroom beds,-0.08955684826394283,0.08955684826394283,negative
	17	+baths,0.08536227353372888,0.08536227353372888,positive
	18	+schlafzimmer,0.08440216072145762,0.08440216072145762,positive
	19	+new bedroom,-0.07917584216465703,0.07917584216465703,negative
	20	+montréal new,-0.07143580086338461,0.07143580086338461,negative
	21	+bromont bedrooms,0.07103164459341331,0.07103164459341331,positive
	22	+montréal studio,0.07066440742120515,0.07066440742120515,positive
	23	+91 bedroom,-0.07007870847545931,0.07007870847545931,negative
	24	+beaupré,-0.06953159908287251,0.06953159908287251,negative
	25	+in beaupré,-0.06935404285820401,0.06935404285820401,negative
	26	+howard,0.06909208902914757,0.06909208902914757,positive
	27	+adolphe howard,0.06909208902914757,0.06909208902914757,positive
	28	+adolphe,0.06909208902914757,0.06909208902914757,positive
	29	+new bedrooms,-0.06663091906009003,0.06663091906009003,negative
	30	+91,-0.06216081063470124,0.06216081063470124,negative
	31	+half,-0.061441381794857856,0.061441381794857856,negative
	32	+half bath,-0.061441381794857856,0.061441381794857856,negative
	33	+61 bedroom,-0.057758992522013705,0.057758992522013705,negative
	34	+97 bedroom,-0.05734723885023703,0.05734723885023703,negative
	35	+93 bedroom,-0.05445591145666726,0.05445591145666726,negative
	36	+beds bath,-0.05338814538467919,0.05338814538467919,negative
	37	+hotel,-0.05279655979793697,0.05279655979793697,negative
	38	+hotel in,-0.05279655979793697,0.05279655979793697,negative
	39	+private,-0.05268922750785858,0.05268922750785858,negative
	40	+bed private,-0.0512627418826572,0.0512627418826572,negative
	41	+beds shared,-0.05124311398508191,0.05124311398508191,negative

added results/tables/price_decomposition.csv +5001 −0

		`@@ -0,0 +1,5001 @@`
	1	+actual_price,predicted_price,prediction_error,base_component,property_component,amenity_component,host_component,review_component,location_component,text_component,spatial_component
	2	+,-5.239542746970883,,0.0,-5.492497740963282,0.006495942692706633,0.0024904528831925232,0.0,0.0,0.0,0.24396859841650084
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	1899	+4.343805421853684,5.467388107549205,-1.1235826856955207,0.0,-1.9541965729541309,0.05006636172628886,0.0024904528831925232,7.574007125526919,0.0,0.0,-0.20497925963306463
	1900	+8.280680672363074,7.112683112570425,1.1679975597926493,0.0,-1.3186036011588405,0.12237360815864244,0.0,8.529170438465806,0.0,0.0,-0.22025733289518348
	1901	+5.172640758460035,6.076340194023357,-0.9036994355633219,0.0,-1.2672644313164039,0.0012242056241416402,0.017324634400523453,7.476386659125333,0.0,0.0,-0.1513308738102371
	1902	+4.770006428441026,6.546776916173412,-1.776770487732386,0.0,-2.042725145280208,0.027236236716126322,0.0,8.166361738780877,0.0,0.0,0.39590408595661697
	1903	+5.612726565927474,4.760102138677382,0.8526244272500918,0.0,-2.1077252656427676,-0.07990315149813247,0.0,7.14649094713844,0.0,0.0,-0.19876039132015744
	1904	+5.160146657489371,5.422391513345866,-0.2622448558564958,0.0,-2.2797906994870347,0.10608595367341861,-0.012124622699479781,7.647282660025731,0.0,0.0,-0.039061778166770006
	1905	+5.213249554157973,5.694426632661937,-0.48117707850396396,0.0,-2.111884918729376,-0.07056417701974345,0.0,8.064753453662535,0.0,0.0,-0.18787772525147875
	1906	+4.783818337415737,6.189727276147381,-1.4059089387316437,0.0,-1.9430120529205546,0.006781555060213981,0.029449257100003234,8.030237282343107,0.0,0.0,0.06627123456461262
	1907	+5.499542453761224,5.692323409390777,-0.19278095562955322,0.0,-2.1055382565293597,-0.08891660551475683,0.0,8.109478440835934,0.0,0.0,-0.22270016940104062
	1908	+9.003193193746572,6.056058279895773,2.9471349138507987,0.0,-2.043044316486802,0.028994864119813057,0.029449257100003234,8.256548952914406,0.0,0.0,-0.21589047775164338
	1909	+,4.113418770501543,,0.0,-5.096068477950249,-0.13073878147205215,0.0,9.238202504200961,0.0,0.0,0.10202352572288395
	1910	+5.043425116919247,5.319976704085842,-0.2765515871665949,0.0,-2.063923435794245,-0.03796182418127922,0.017324634400523453,7.383244082297789,0.0,0.0,0.02129324736305159
	1911	+5.003946305945459,5.750759973519793,-0.746813667574334,0.0,-1.7503347044576278,-0.15506762782717412,0.017324634400523453,7.662839605421093,0.0,0.0,-0.024001934017018278
	1912	+,6.270183276159578,,0.0,-1.8924666901907568,-0.1761714052308943,0.0,8.368705490143888,0.0,0.0,-0.029884118562660985
	1913	+5.318119993844216,4.999986398837463,0.31813359500675276,0.0,-4.019080716057488,0.027885484556860435,0.0,9.0784399922403,0.0,0.0,-0.08725836190220745
	1914	+5.220355825078324,5.189329543336224,0.031026281742100537,0.0,-2.171572948365468,-0.1790795677581699,0.026786096644932214,7.740842286090309,0.0,0.0,-0.22764632327538045
	1915	+5.204006687076795,4.7012706497972205,0.5027360372795746,0.0,-1.9246321724156585,-0.2684065030315114,0.026786096644932214,7.012520210822082,0.0,0.0,-0.14499698222262422
	1916	+,5.880337711644524,,0.0,-1.3328045872012364,0.1146940385673049,0.029449257100003234,7.247826299608728,0.0,0.0,-0.1788272964302771
	1917	+6.103542450513332,5.947880357130163,0.15566209338316916,0.0,-1.3712071591635775,-0.06526151554193213,0.038910719344411995,7.394969742760559,0.0,0.0,-0.04953143026929638
	1918	+3.8571443439181565,6.0064847852467445,-2.149340441328588,0.0,-2.2482028850798876,-0.05064002319838455,0.0,8.32830968484286,0.0,0.0,-0.022981991317844175
	1919	+6.766802127398923,6.282214039962051,0.48458808743687154,0.0,-1.8331973129391974,0.044333076931428746,0.029449257100003234,7.85857300810844,0.0,0.0,0.18305601076137645
	1920	+,5.826295094878384,,0.0,-1.3827186261827,-0.04494055098100976,0.04140117222760452,7.383255583784398,0.0,0.0,-0.1707024839699079
	1921	+6.603537237890257,-5.3316017675324145,11.935139005422672,0.0,-5.209506387342814,0.03273921980195708,0.0,0.0,0.0,0.0,-0.15483459999155783
	1922	+4.8895969657192,5.321013775171154,-0.43141680945195393,0.0,-1.687972613570646,-0.13247951971134025,0.019815087283715976,7.325231799279644,0.0,0.0,-0.2035809781102169
	1923	+,5.685167859302616,,0.0,-2.9455024475350955,0.009863182161578471,0.029449257100003234,8.385559767161364,0.0,0.0,0.20579810041476643
	1924	+5.455919227051527,5.801203995144072,-0.3452847680925455,0.0,-1.5097579192336097,-0.022991143126816545,0.038910719344411995,7.151852790928522,0.0,0.0,0.14318954723156527
	1925	+8.332019571353657,6.062843679810552,2.269175891543105,0.0,-1.3666543084640947,-0.0743449978305962,0.0,7.794077106820913,0.0,0.0,-0.29023412071566723
	1926	+,6.019963364281106,,0.0,-1.9648057181549934,-0.0005643732451753765,-0.009634169816287258,8.036789051724245,0.0,0.0,-0.04182142622668222
	1927	+,5.65455208215337,,0.0,-2.288936813070296,0.09356798623020027,0.026786096644932214,7.859206353747091,0.0,0.0,-0.036071541398556645
	1928	+6.91752737613084,4.456680742183563,2.460846633947277,0.0,-4.572808480668433,0.043341439654360076,0.0024904528831925232,9.238202504200961,0.0,0.0,-0.25454517388651743
	1929	+5.377913820819179,4.96668126895424,0.41123255186493957,0.0,-1.8381131008454137,-0.08969878113139847,0.026786096644932214,6.858785166836273,0.0,0.0,0.008921887449846067
	1930	+4.564348191467836,5.954857042626434,-1.3905088511585975,0.0,-2.0012488445501164,-0.031571522547699266,0.026786096644932214,8.00227472530355,0.0,0.0,-0.041383412224233765
	1931	+,3.4811889590654146,,0.0,-5.23607674553439,-0.06888926923216619,0.0,9.0784399922403,0.0,0.0,-0.29228501840832966
	1932	+5.937958279584841,5.686739058506481,0.2512192210783599,0.0,-1.7213219545836598,0.04519043147368229,0.017324634400523453,7.513066411664087,0.0,0.0,-0.1675204644481513
	1933	+7.01580218306527,6.942989112391942,0.07281307067332854,0.0,-0.7292221088798465,-0.10718567432851864,0.0,8.00886278593909,0.0,0.0,-0.2294658903387879
	1934	+,5.8424977015038895,,0.0,-1.696757187729684,-0.1458214127932371,0.017324634400523453,7.722960577795597,0.0,0.0,-0.05520891016930981
	1935	+5.7985773702092445,5.711167229728056,0.08741014048118867,0.0,-1.7319619255275978,-0.07943189727452714,0.026786096644932214,7.610719519929885,0.0,0.0,-0.11494456404463615
	1936	+5.603188359420499,4.837519774380178,0.7656685850403209,0.0,-1.8982847832695668,-0.18399046513548256,0.019815087283715976,6.889750547569616,0.0,0.0,0.01022938793189683
	1937	+6.2877470297789335,5.189329450934103,1.0984175788448303,0.0,-1.8855443622515695,-0.08372911454125026,0.0,7.2987619162095285,0.0,0.0,-0.14015898848260477
	1938	+,7.262289299675044,,0.0,-0.6848868107845965,-0.007398075079581214,-0.009634169816287258,7.99718864423207,0.0,0.0,-0.03298028887656099
	1939	+7.424570926680338,5.965603047060312,1.458967879620026,0.0,-1.7826602973368368,0.019402229438207897,0.0,7.720551108471027,0.0,0.0,0.008310006487914329
	1940	+7.544707698886281,4.742069016052778,2.8026386828335035,0.0,-2.53503814931904,0.012527991657007922,0.0,7.374901974949108,0.0,0.0,-0.11032280123429818
	1941	+4.564348191467836,5.078175851759264,-0.5138276602914278,0.0,-1.6690794670376379,0.00369284822342366,0.0024904528831925232,6.760130888425332,0.0,0.0,-0.019058870735045058
	1942	+,5.236815627577542,,0.0,-2.199690638947505,-0.010525169851748,0.026786096644932214,7.586832034485701,0.0,0.0,-0.16658669475383625
	1943	+,5.446898555199504,,0.0,-2.0477284956442556,0.08641458983098692,0.017324634400523453,7.586461285050683,0.0,0.0,-0.19557345843843485
	1944	+,5.492631486334823,,0.0,-2.203309168302786,0.06252422857375287,0.017324634400523453,7.859206353747091,0.0,0.0,-0.2431145620837588
	1945	+6.45833828334479,5.975373350621125,0.4829649327236645,0.0,-2.4883453138581624,-0.11479133562748103,0.0,8.629475351904443,0.0,0.0,-0.05096535179767345
	1946	+5.152482018324492,5.842823048478679,-0.6903410301541868,0.0,-2.0723012728539603,-0.0251587873280399,0.0,8.065742118822579,0.0,0.0,-0.12545901016190134
	1947	+,5.857090073433337,,0.0,-2.0657544669596213,-0.14210943474139068,0.0,7.936366950759277,0.0,0.0,0.12858702437507155
	1948	+4.539564428890097,4.87094337812747,-0.3313789492373731,0.0,-2.04605442792269,-0.11869050237614051,0.026786096644932214,7.088558120228088,0.0,0.0,-0.07965590844671759
	1949	+5.458393314394671,5.358067563296653,0.10032575109801822,0.0,-2.027513481748591,-0.06053177497388271,0.029276549528124737,7.662468855986076,0.0,0.0,-0.24563258549507527
	1950	+9.15794225697272,6.140926770171994,3.0170154868007266,0.0,-1.4369950402691218,-0.18702276597074186,-0.012124622699479781,7.776687244171876,0.0,0.0,0.0003819549394623589
	1951	+5.568726110329361,5.9580855927587635,-0.3893594824294029,0.0,-1.60268034222101,-0.020306768902998856,0.0,7.822109987350824,0.0,0.0,-0.2410372834680499
	1952	+5.224024761966182,4.567649354037951,0.6563754079282305,0.0,-2.2482975432176433,-0.191399129563719,0.0,7.6381283240238815,0.0,0.0,-0.6307822972045704
	1953	+4.911330479685645,5.0596167559030105,-0.14828627621736512,0.0,-1.9427001100180448,-0.14062486051378084,0.017324634400523453,7.325231799279644,0.0,0.0,-0.19961470724532998
	1954	+,6.597357620659668,,0.0,-1.4251812598729368,-0.1455613041629802,0.04140117222760452,8.222776125363746,0.0,0.0,-0.09607711289576334
	1955	+7.81442790481035,6.059835620948518,1.7545922838618324,0.0,-2.5045772987974164,-0.08788187962217309,0.03193970998319576,8.368542844490106,0.0,0.0,0.25181224489480614
	1956	+4.5529290741424004,4.9125507307474745,-0.35962165660507406,0.0,-1.3146872239246887,-0.013546440457305561,0.026786096644932214,6.511917044440897,0.0,0.0,-0.29791874595635826
	1957	+4.254477313726906,5.35021549544357,-1.0957381817166638,0.0,-1.7066552523208838,-0.1515581005047909,0.029449257100003234,7.374166841894308,0.0,0.0,-0.19518725072506582
	1958	+4.991452474679482,5.378741913085376,-0.3872894384058938,0.0,-2.12849118777429,-0.027154162012894188,0.017324634400523453,7.531412244833739,0.0,0.0,-0.01434961636170388
	1959	+3.819469023817538,5.354831337719142,-1.5353623139016044,0.0,-2.7790106667205414,-0.0632209273732384,0.0,8.36899811456135,0.0,0.0,-0.1719351827484292
	1960	+,6.0607930632946125,,0.0,-1.2316786037188168,0.10237068286715165,0.029276549528124737,6.9857985242959195,0.0,0.0,0.1750259103222346
	1961	+7.618521331630536,5.4550693055532795,2.1634520260772563,0.0,-2.4850231573672845,-0.018105290154866602,0.0,8.002388028873966,0.0,0.0,-0.04419027579853536
	1962	+7.621454805728808,-4.187147002526971,11.80860180825578,0.0,-4.436142010000404,0.09306127489408307,0.04140117222760452,0.0,0.0,0.0,0.11453256035174506
	1963	+9.216972331834231,6.17764862349108,3.0393237083431517,0.0,-1.7621651947637185,-0.17013646011978972,0.0,8.210634079704526,0.0,0.0,-0.10068380132993848
	1964	+7.229121129398626,7.51364277938124,-0.2845216499826142,0.0,-0.6681853306629337,0.027527880768902196,0.04140117222760452,8.134852470349093,0.0,0.0,-0.021953413301424612
	1965	+,7.313946960552844,,0.0,-0.6313817675086169,-0.05995321952881279,0.0024904528831925232,8.368705490143888,0.0,0.0,-0.36591399543680797
	1966	+4.941713848629834,5.841442122984934,-0.8997282743551,0.0,-2.2795391757543895,-0.1832516458113887,0.038910719344411995,7.860377237622609,0.0,0.0,0.40494498758369085
	1967	+6.565264970035361,6.79630076444698,-0.2310357944116186,0.0,-1.511361822537622,-0.04476861078154323,0.038910719344411995,8.529170438465806,0.0,0.0,-0.21564996004407125
	1968	+7.702131452200457,6.464206627417921,1.237924824782536,0.0,-1.611198342346417,-0.10582707768217739,0.0,8.368705490143888,0.0,0.0,-0.18747344269737212
	1969	+,6.339545841126143,,0.0,-1.4493078241787183,0.10833689790397,0.0024904528831925232,7.42557109487135,0.0,0.0,0.25245521964634987
	1970	+,5.2649588294532546,,0.0,-1.8215617884390083,-0.18221476447580537,0.0,7.379996471206005,0.0,0.0,-0.11126108883793785
	1971	+,4.756117456182694,,0.0,-2.122740651527817,-0.09499028132962989,0.017324634400523453,7.014363200198287,0.0,0.0,-0.05783944555866913
	1972	+6.62087288670092,6.192842510077063,0.4280303766238571,0.0,-1.5321169853464838,-0.04388330712355948,0.0,7.808574489913721,0.0,0.0,-0.039731687366613797
	1973	+8.212204893063756,6.03959303398114,2.1726118590826164,0.0,-2.2973311807079195,-0.07975477611531885,0.0,8.689511803642718,0.0,0.0,-0.27283281283833893
	1974	+8.245494698265654,6.15226740427516,2.093227293990494,0.0,-2.0718921761010227,0.0909724494143718,0.0,8.20915108196446,0.0,0.0,-0.0759639510026479
	1975	+8.491054533806542,6.117394660688928,2.3736598731176146,0.0,-1.5240491705088486,0.0386918178085267,0.0,7.633479859556787,0.0,0.0,-0.03072784616753882
	1976	+4.704925019125084,4.856508224204599,-0.15158320507951473,0.0,-2.2047326662361417,-0.11715226022467407,0.038910719344411995,7.189584431486167,0.0,0.0,-0.050102000165163176
	1977	+5.623150704630611,5.2557067095073755,0.3674439951232351,0.0,-1.8415225913002535,0.03153512561830129,0.017324634400523453,7.098410331596648,0.0,0.0,-0.05004079080784377
	1978	+,5.153817315633432,,0.0,-2.006366465024338,-0.0835315361814783,0.017324634400523453,7.503324259750442,0.0,0.0,-0.27693357731171697
	1979	+5.653365988742185,5.472327967653411,0.18103802108877431,0.0,-1.664097860584583,0.0011496021858540748,-0.012124622699479781,7.29472806442203,0.0,0.0,-0.14732721567040966
	1980	+,4.566910525859729,,0.0,-2.146371283866883,-0.23231963096869443,0.026786096644932214,7.101545356342906,0.0,0.0,-0.18273001229253064
	1981	+5.343577497952901,5.499550853028136,-0.15597335507523535,0.0,-1.687110030141607,-0.029066336720920235,0.029276549528124737,7.5639395647049685,0.0,0.0,-0.37748889434242994
	1982	+6.609025740711332,6.033753265004842,0.5752724757064902,0.0,-1.5197627617235634,-0.11282837739304188,0.0,7.7619430695627445,0.0,0.0,-0.0955986654412969
	1983	+6.582772781775894,5.8412434750092075,0.7415293067666866,0.0,-1.166747383624756,0.03370908157499026,0.017324634400523453,7.229714109038997,0.0,0.0,-0.27275696638054514
	1984	+8.030503928000046,6.142973324708239,1.8875306032918067,0.0,-2.090943001490486,-0.03136176709331158,0.017324634400523453,8.193544643000152,0.0,0.0,0.05440881589136318
	1985	+7.621454805728808,7.504822471769003,0.11663233395980477,0.0,-0.9391587620729309,0.047034607376881324,0.04140117222760452,8.134852470349093,0.0,0.0,0.2206929838883561
	1986	+4.525477417078843,6.2341464902308195,-1.7086690731519765,0.0,-1.8851858524559386,-0.03155875322254605,0.0,7.855607012628308,0.0,0.0,0.29528408328099753
	1987	+,5.040776220853716,,0.0,-2.4041430356776523,-0.11453192289221617,0.0,7.72270344566868,0.0,0.0,-0.16325226624509556
	1988	+5.632680237163052,6.923785095511085,-1.2911048583480325,0.0,-1.067398158567257,0.0533409241526316,0.029276549528124737,7.740842286090309,0.0,0.0,0.16772349430727498
	1989	+7.406315995328056,6.768278739357273,0.638037255970783,0.0,-1.3296213829062475,-0.15208590514157497,0.0,8.308669038405613,0.0,0.0,-0.05868301100051737
	1990	+,4.740257427458308,,0.0,-2.40333183887941,-0.08479869704751361,0.026786096644932214,7.331494698243385,0.0,0.0,-0.12989283150308797
	1991	+5.0747987449037435,5.055346025102675,0.019452719801068774,0.0,-2.0180506778879126,0.012066605001551586,0.038910719344411995,7.095754838992251,0.0,0.0,-0.07333546034762871
	1992	+6.4605615154609195,4.161677641092923,2.2988838743679967,0.0,-5.074949516830932,0.013359237947491245,0.029449257100003234,9.238202504200961,0.0,0.0,-0.04438384132459873
	1993	+6.433325870407437,5.1361728052697835,1.2971530651376533,0.0,-1.7225962857912815,-0.08387095265855611,0.029449257100003234,7.016472096992067,0.0,0.0,-0.10328131037244961
	1994	+5.671465873179658,5.914241502451195,-0.24277562927153706,0.0,-1.5723977036551995,0.001159007844686137,0.019815087283715976,7.253647709067453,0.0,0.0,0.21201740191054005
	1995	+4.525477417078843,4.853051842047874,-0.3275744249690309,0.0,-2.587572304110725,-0.02426787821507426,0.026786096644932214,7.692534171389461,0.0,0.0,-0.2544282436607193
	1996	+5.282340412024896,5.362456324071459,-0.08011591204656376,0.0,-1.6703186499930087,-0.10073970111956285,0.038910719344411995,7.25111532555368,0.0,0.0,-0.156511369714063
	1997	+,-5.506393053630012,,0.0,-5.61864643909381,-0.07016642625627638,0.029449257100003234,0.0,0.0,0.0,0.15297055462007186
	1998	+,-2.544682186759255,,0.0,-1.9385584590574645,-0.02736784471718958,0.029449257100003234,-0.4356690813137208,0.0,0.0,-0.17253605877088354
	1999	+,-2.565136245520946,,0.0,-1.660609325154839,0.03866214805303374,0.029449257100003234,-0.7423105607057928,0.0,0.0,-0.23032776481335118
	2000	+,-5.074415818138829,,0.0,-4.834436027942825,-0.055416135061877245,0.0024904528831925232,0.0,0.0,0.0,-0.18705410801732003
	2001	+,-3.036012072361127,,0.0,-2.4234420726342263,-0.07779059465365229,0.0,-0.4364105801837537,0.0,0.0,-0.09836882488949394
	2002	+,-3.2421460972737153,,0.0,-2.3934085678913375,0.010528748284603213,0.017324634400523453,-0.7426813101408093,0.0,0.0,-0.13390960192669438
	2003	+,-5.144822656081784,,0.0,-5.461476376716176,0.03373912530324595,0.0,0.0,0.0,0.0,0.2829145953311449
	2004	+4.204692619390966,-2.9681241071483204,7.172816726539287,0.0,-2.657828420605524,0.00558680633202123,0.0,-0.276687130731869,0.0,0.0,-0.03919536214294875
	2005	+4.394449154672439,-3.2314288049885334,7.625877959660972,0.0,-2.104030102738508,-0.05598742043154152,0.0,-0.5976170273757043,0.0,0.0,-0.47379425444277934
	2006	+,-3.0307307489004547,,0.0,-2.0108860973542972,0.016616814145919194,0.029276549528124737,-0.7423105607057928,0.0,0.0,-0.32342745451440874
	2007	+,-2.598509287427447,,0.0,-1.9242825995392634,-0.06846432615921576,0.029449257100003234,-0.4357926644587263,0.0,0.0,-0.19941895437024376
	2008	+,-2.4073104783166284,,0.0,-1.55880372082754,0.012735137021147841,-0.012124622699479781,-0.7423105607057928,0.0,0.0,-0.10680671110496176
	2009	+,-2.6173996916920017,,0.0,-2.1407718062276535,0.08879676912118699,0.038910719344411995,-0.5978641936657153,0.0,0.0,-0.006471180264231199
	2010	+5.204006687076795,5.642113725953056,-0.4381070388762609,0.0,-1.93722722606749,-0.0305636668834667,0.017324634400523453,7.740842286090309,0.0,0.0,-0.14826230158681966
	2011	+5.030437921392435,4.7814822862251045,0.24895563516733077,0.0,-2.443138851557168,0.0629501460581454,0.017324634400523453,7.330382449938336,0.0,0.0,-0.18603609261473128
	2012	+5.25227342804663,6.011285814721402,-0.7590123866747724,0.0,-1.682764076691166,-0.006054079029434348,0.029449257100003234,7.6786063400572155,0.0,0.0,-0.007951626715213406
	2013	+,6.31851464416861,,0.0,-1.7825714403021697,0.06529657844088503,0.0,8.049180466222564,0.0,0.0,-0.013390960192669438
	2014	+4.465908118654584,4.4721210012780634,-0.006212882623479743,0.0,-2.3984526535837523,-0.015868121361213808,0.038910719344411995,6.927568441893596,0.0,0.0,-0.08003738501497748
	2015	+7.776954403322442,4.8782989418505,2.898655461471942,0.0,-4.096583831985468,-0.12449143180804191,0.0024904528831925232,9.0784399922403,0.0,0.0,0.01844376052051863
	2016	+4.836281906951478,5.278261427291736,-0.4419795203402579,0.0,-2.4388929375675747,-0.09230324472591978,0.0,7.916585291126586,0.0,0.0,-0.1071276815413555
	2017	+8.871785969150645,5.30115841169719,3.570627557453455,0.0,-2.4133485478878525,-0.020862448448627738,0.0024904528831925232,8.00065786484389,0.0,0.0,-0.2677789096934132
	2018	+5.594711379601839,5.209674553003892,0.3850368265979469,0.0,-1.9560353532880326,-0.12074740224402862,0.026786096644932214,7.44258537165091,0.0,0.0,-0.18291415975988762
	2019	+4.718498871295094,5.554481200410136,-0.8359823291150414,0.0,-2.1574541029550787,-0.17104970625785126,0.0,7.855938699554548,0.0,0.0,0.02704631006851746
	2020	+6.656726524178391,6.279189118996784,0.3775374051816076,0.0,-2.0445582256110804,0.022794433464692825,0.0,8.41763181696135,0.0,0.0,-0.11667890581817829
	2021	+,5.8576223731324895,,0.0,-2.0199923156033153,-0.07955757838639059,0.0,7.476359884661096,0.0,0.0,0.4808123824611
	2022	+5.480638923341991,5.494870726747691,-0.01423180340569985,0.0,-1.0472852945266782,-0.08643424875647637,0.017324634400523453,6.618998085209933,0.0,0.0,-0.007732449579609835
	2023	+5.948034989180646,5.767991382301214,0.18004360687943155,0.0,-1.6196053457502009,-0.10698630626722996,0.029276549528124737,7.089546785388132,0.0,0.0,0.3757596994023904
	2024	+7.728855823852543,5.5323652095692,2.196490614283343,0.0,-1.1877948640693772,0.019439087569050578,0.026786096644932214,6.576858119816345,0.0,0.0,0.09707676960824974
	2025	+5.213249554157973,5.845979027466849,-0.632729473308876,0.0,-1.9124834705917957,-0.04857354470614995,0.029449257100003234,7.835943134531343,0.0,0.0,-0.058356348866552055
	2026	+8.4272881773486,4.034189481211632,4.393098696136969,0.0,-5.729493631860889,-0.04962213861027806,0.0,9.0784399922403,0.0,0.0,0.7348652594425014
	2027	+,4.867017106509484,,0.0,-2.6739956079779423,-0.10884598580893963,0.026786096644932214,7.740224370365282,0.0,0.0,-0.11715176671385039
	2028	+4.738564069158622,4.661485455794824,0.07707861336379818,0.0,-2.5560215931856805,-0.1304370605288785,0.026786096644932214,7.521807199755189,0.0,0.0,-0.20064918689073738
	2029	+,4.818187641332136,,0.0,-1.9040038178353145,0.045575138663875574,0.038910719344411995,7.0467914452587745,0.0,0.0,-0.4090858440996118
	2030	+4.99991133073328,4.9999113307359115,-2.631672657571471e-12,0.0,-3.20057694639791,-0.054029367690855104,0.017324634400523453,7.858464854877058,0.0,0.0,0.37872815554709705
	2031	+8.433226628816453,6.065107221439256,2.368119407377197,0.0,-1.2915477256525945,-0.11577710154961264,0.03193970998319576,7.193127652087397,0.0,0.0,0.247364686570869
	2032	+7.15445909470536,6.573216430477531,0.5812426642278288,0.0,-0.729335562202925,0.08456025701994552,-0.012124622699479781,7.375773422779379,0.0,0.0,-0.14565706441938744
	2033	+6.976011879488119,6.287758915978282,0.6882529635098376,0.0,-1.7434680220322667,-0.11130356674351685,0.038910719344411995,7.935461090972494,0.0,0.0,0.1681586944371602
	2034	+8.404317843336385,-4.364586128152142,12.768903971488527,0.0,-4.295960826845755,-0.027904000916841337,0.0024904528831925232,0.0,0.0,0.0,-0.04321175327273802
	2035	+7.1631723908466425,5.762339899432796,1.4008324914138468,0.0,-1.9084380898901276,-0.01047122184162096,0.0,7.369612155993707,0.0,0.0,0.31163705517083684
	2036	+8.950457625993279,6.246010936863891,2.7044466891293872,0.0,-1.8064630975322258,-0.1106672578590366,0.0,8.329831745091703,0.0,0.0,-0.16669045283654876
	2037	+6.05622179432326,6.66565996631715,-0.6094381719938893,0.0,-1.7749998775045244,0.1224052944005127,0.029449257100003234,8.469010403582525,0.0,0.0,-0.18020511126136524
	2038	+8.50039692049865,5.95592592513942,2.54447099535923,0.0,-1.9505933805527311,-0.10270892040307888,0.0,8.047697468482498,0.0,0.0,-0.038469242387270196
	2039	+8.660493215219224,6.532986910785769,2.1275063044334557,0.0,-1.5234385708394829,0.06858596673802328,0.019815087283715976,7.740224370365282,0.0,0.0,0.2278000572382308
	2040	+7.800367883501715,6.965694858289593,0.8346730252121217,0.0,-0.7231213342344782,-0.20594333779849677,0.029276549528124737,7.795911683046656,0.0,0.0,0.06957129774778767
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	2042	+5.58052196499145,5.538527421536463,0.04199454345498754,0.0,-1.7315365522250987,-0.07298924167325194,0.017324634400523453,7.38997095357968,0.0,0.0,-0.06424237254538888
	2043	+4.732067119176442,5.232471157913791,-0.500404038737349,0.0,-2.4164419444223446,-0.08080623364338298,0.017324634400523453,7.965649989520884,0.0,0.0,-0.2532552879418888
	2044	+4.47688216568656,4.012770277461586,0.4641118882249744,0.0,-1.9135903094107194,0.14217899743498627,0.029449257100003234,6.786148095044595,0.0,0.0,-1.0314157627072793
	2045	+3.9541241814467813,4.651579562924815,-0.6974553814780338,0.0,-2.3582112732086182,-0.16599339320513806,0.017324634400523453,7.404300635889312,0.0,0.0,-0.24584104095126472
	2046	+7.718334322433416,6.2387541101857575,1.479580212247659,0.0,-1.427098354037926,-0.08322403459200381,0.038910719344411995,7.32394208905197,0.0,0.0,0.3862236904193068
	2047	+5.895641735930645,6.039118824311627,-0.1434770883809824,0.0,-1.2743427435233352,-0.07030149248608991,0.04140117222760452,7.15561123757311,0.0,0.0,0.18675065052033865
	2048	+4.699388861227073,6.09235261386331,-1.3929637526362377,0.0,-1.4499502392919483,-0.07625262051688178,0.0024904528831925232,7.631380790990429,0.0,0.0,-0.015315770201480311
	2049	+6.191666951120625,5.175440699094204,1.0162262520264207,0.0,-2.1038482278543382,-0.07535646777690741,0.017324634400523453,7.318565033316901,0.0,0.0,0.018755727008027273
	2050	+,5.572541888771753,,0.0,-1.5575283463848164,0.03529258859373217,0.0,6.9944685343696715,0.0,0.0,0.10030911219316607
	2051	+,5.4270865293832085,,0.0,-2.3753176723924705,-0.12458826837204379,0.017324634400523453,7.936449756132538,0.0,0.0,-0.026781920385338876
	2052	+,4.875861596422799,,0.0,-2.238836131265809,-0.09364664958430964,0.017324634400523453,7.2791628430342765,0.0,0.0,-0.0881431001618845
	2053	+,4.8929042556756865,,0.0,-2.087993265951618,-0.17538969031894125,0.019815087283715976,7.119647497363626,0.0,0.0,0.016824627298902856
	2054	+5.700945119836041,5.331593366112847,0.3693517537231932,0.0,-1.523333050185836,0.04165540973968458,0.017324634400523453,6.988950357669508,0.0,0.0,-0.1930039855110318
	2055	+5.078231635333065,6.025360981696094,-0.9471293463630293,0.0,-1.7123756937358587,0.040745386593862426,0.019815087283715976,7.758357295661739,0.0,0.0,-0.08118109410736435
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	2057	+,6.06284823944006,,0.0,-1.6288869876419074,0.04878081629078755,0.017324634400523453,7.635778421650977,0.0,0.0,-0.010148645260321993
	2058	+,4.654905853262759,,0.0,-2.1373820944243844,-0.14961854738776897,0.026786096644932214,7.069333681353308,0.0,0.0,-0.15421328292332698
	2059	+5.190509413913731,6.0175395369554785,-0.8270301230417472,0.0,-1.758473191294887,-0.04003774894304648,0.0,7.747004039892409,0.0,0.0,0.06904643730100243
	2060	+7.413795470811572,5.668710586310087,1.7450848845014857,0.0,-1.7604549647866339,-0.15711307960108245,0.0,7.839575000796945,0.0,0.0,-0.2532963700991402
	2061	+4.254477313726906,5.2933453740327385,-1.0388680603058322,0.0,-2.2248501623386376,-0.08820057937437076,0.017324634400523453,7.573185786376364,0.0,0.0,0.015885694968860983
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	2063	+5.527681529675303,5.25635240305031,0.27132912662499287,0.0,-1.655230461196882,0.00551615001791899,0.017324634400523453,6.903489540256323,0.0,0.0,-0.014747460427573049
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	2065	+8.155089251396877,5.604629757640819,2.5504594937560583,0.0,-1.5376297582095217,-0.010448566993147536,0.017324634400523453,7.184653716105893,0.0,0.0,-0.04927026766292545
	2066	+5.681195975983405,5.482995623976684,0.1982003520067206,0.0,-1.678820078484832,-0.22133659360110397,0.017324634400523453,7.286273533591229,0.0,0.0,0.0795541280708687
	2067	+9.389427537093473,3.663139452011672,5.726288085081801,0.0,-5.180501779843789,-0.02504744203616977,0.0,9.0784399922403,0.0,0.0,-0.20975131834866742
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	2070	+4.844187086458591,6.013876663695951,-1.1696895772373601,0.0,-2.202174951557634,-0.047141921444438996,0.0,8.41763181696135,0.0,0.0,-0.1544382802633259
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added results/tables/price_premiums.csv +57 −0

		`@@ -0,0 +1,57 @@`
	1	+,coefficient,std_error,p_value,premium_pct,premium_lo_95,premium_hi_95
	2	+has_reviews,9.511318322332384,3.738460089109724,0.010953437737680097,1351079.548181805,788.239466415744,2055398517.6631637
	3	+text_embed_0,2.800147844902441,0.43000945468704777,7.424142976981857e-11,1544.7078208027947,608.0501940221127,3720.440752150118
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	11	+cleanliness_premium,0.20729084896040958,0.11012116701105469,0.05978324851434462,23.03403629621514,-0.8506287373553301,52.67241632062183
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	29	+cozy_language_score,2.3338013479642686e-15,3.5609857800132175e-14,0.9477455961163753,2.4424906541753444e-13,-6.750155989720952e-12,7.2164496600635175e-12
	30	+distance_to_center,-5.245213604210112e-06,2.8389150727956524e-06,0.06465849000188388,-0.0005245199848125104,-0.0010809326480898385,3.189577444384639e-05
	31	+local_avg_price_1km,-9.066245962433865e-05,5.48830356665867e-05,0.09855058047471184,-0.009065834990773514,-0.01982115863804479,0.0016906456556942118
	32	+accommodates_sq,-0.005362698604160734,0.007624317657832579,0.48182664426864397,-0.5348345005471322,-2.010130648004538,0.9626731136373579
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	35	+is_guest_favorite,-0.012124622699444199,0.1022872314171178,0.9056437202468889,-1.2051415629708906,-19.152369872024988,20.726161523135424
	36	+guest_favorite_flag,-0.012124622699479781,0.1022872314171166,0.9056437202466121,-1.20514156297441,-19.152369872027673,20.72616152313085
	37	+spatial_cluster,-0.013390960192669438,0.005025555201163783,0.007708606538347646,-1.330170015530907,-2.297287854312813,-0.353479086160724
	38	+quality_score,-0.017101693470780666,0.01801381864786639,0.34243487707780784,-1.695628957520856,-5.1058502976592095,1.8371458764336435
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	42	+text_embed_2,-0.08534284931388031,0.4600115378365322,0.8528186224397026,-8.180257292764647,-62.72878585218449,126.20312602070243
	43	+lat,-0.08973780290976088,0.045383722596035875,0.048006315299874834,-8.58291532134119,-16.3633416234674,-0.07870312654709721
	44	+remote_work_index,-0.09000506644644435,0.5682260244938251,0.8741442981257269,-8.607344510044602,-69.99204310118597,178.3467566842508
	45	+large_capacity_listing,-0.12493844960018569,0.14797280729651827,0.3984828775174668,-11.744877770653284,-33.9633188001592,17.9490922044657
	46	+location_rating_premium,-0.1459293647801434,0.11603886727633216,0.20853992928805132,-13.578125469016545,-31.15835820175762,8.49160772978157
	47	+bathrooms,-0.16442622939119067,0.22631251141508918,0.46750471561731355,-15.1619659829894,-45.555622456626566,32.19899539006619
	48	+review_quality,-0.1645431921070989,0.13513793492411155,0.22337814547176382,-15.171888289579073,-34.91061308631078,10.55271646509044
	49	+tourism_language_score,-0.2087260508305275,0.11499140935932874,0.06950167058755337,-18.838245285603495,-35.2155818184504,1.679240367028334
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	51	+amenity_richness_index,-0.26915956904315474,0.10579930358735345,0.01095728757988842,-23.597866823204082,-37.90609771040733,-5.992605735406897
	52	+log_reviews,-0.39999765194272413,0.06572429097063069,1.1577065166839158e-09,-32.96783800126516,-41.06970582166759,-23.752107385937006
	53	+bathrooms_per_guest,-0.4407759761226667,0.33469252489447626,0.18785272535237096,-35.64631419521861,-66.60490893171496,24.012145024319498
	54	+professional_tone_score,-0.62262548147976,19.506981150630864,0.9745373762829133,-46.34660731182546,-100.0,2.15759361375148e+18
	55	+log_accommodates,-1.0943003364710635,1.1354499493501427,0.3351666656218816,-66.52262459868967,-96.38378158695701,209.91896388724487
	56	+business_travel_index,-1.1561530043644892,0.5602060905171665,0.03903673815109476,-68.53055164704183,-89.50358867376636,-5.650974522655772
	57	+log_bedrooms,-2.053249572706008,0.7740341688728041,0.007986023976347761,-87.16827508744086,-97.18533986850318,-41.501582237558175

added results/tables/price_premiums.html +519 −0

		`@@ -0,0 +1,519 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>coefficient</th>
	6	+ <th>std_error</th>
	7	+ <th>p_value</th>
	8	+ <th>premium_pct</th>
	9	+ <th>premium_lo_95</th>
	10	+ <th>premium_hi_95</th>
	11	+ </tr>
	12	+ </thead>
	13	+ <tbody>
	14	+ <tr>
	15	+ <th>has_reviews</th>
	16	+ <td>9.511318e+00</td>
	17	+ <td>3.738460e+00</td>
	18	+ <td>1.095344e-02</td>
	19	+ <td>1.351080e+06</td>
	20	+ <td>7.882395e+02</td>
	21	+ <td>2.055399e+09</td>
	22	+ </tr>
	23	+ <tr>
	24	+ <th>text_embed_0</th>
	25	+ <td>2.800148e+00</td>
	26	+ <td>4.300095e-01</td>
	27	+ <td>7.424143e-11</td>
	28	+ <td>1.544708e+03</td>
	29	+ <td>6.080502e+02</td>
	30	+ <td>3.720441e+03</td>
	31	+ </tr>
	32	+ <tr>
	33	+ <th>bedrooms</th>
	34	+ <td>1.140714e+00</td>
	35	+ <td>3.107383e-01</td>
	36	+ <td>2.416228e-04</td>
	37	+ <td>2.129001e+02</td>
	38	+ <td>7.017856e+01</td>
	39	+ <td>4.753163e+02</td>
	40	+ </tr>
	41	+ <tr>
	42	+ <th>rating</th>
	43	+ <td>1.051601e+00</td>
	44	+ <td>4.938310e-01</td>
	45	+ <td>3.321490e-02</td>
	46	+ <td>1.862231e+02</td>
	47	+ <td>8.731384e+00</td>
	48	+ <td>6.534498e+02</td>
	49	+ </tr>
	50	+ <tr>
	51	+ <th>family_comfort_index</th>
	52	+ <td>9.212190e-01</td>
	53	+ <td>5.881231e-01</td>
	54	+ <td>1.172618e-01</td>
	55	+ <td>1.512351e+02</td>
	56	+ <td>-2.066424e+01</td>
	57	+ <td>6.955943e+02</td>
	58	+ </tr>
	59	+ <tr>
	60	+ <th>log_bathrooms</th>
	61	+ <td>6.445170e-01</td>
	62	+ <td>5.405862e-01</td>
	63	+ <td>2.331609e-01</td>
	64	+ <td>9.050667e+01</td>
	65	+ <td>-3.396679e+01</td>
	66	+ <td>4.496142e+02</td>
	67	+ </tr>
	68	+ <tr>
	69	+ <th>text_embed_1</th>
	70	+ <td>4.786851e-01</td>
	71	+ <td>4.423219e-01</td>
	72	+ <td>2.791593e-01</td>
	73	+ <td>6.139509e+01</td>
	74	+ <td>-3.217572e+01</td>
	75	+ <td>2.840567e+02</td>
	76	+ </tr>
	77	+ <tr>
	78	+ <th>luxury_index</th>
	79	+ <td>4.453708e-01</td>
	80	+ <td>5.705044e-01</td>
	81	+ <td>4.350017e-01</td>
	82	+ <td>5.610689e+01</td>
	83	+ <td>-4.897208e+01</td>
	84	+ <td>3.775692e+02</td>
	85	+ </tr>
	86	+ <tr>
	87	+ <th>luxury_language_score</th>
	88	+ <td>2.184082e-01</td>
	89	+ <td>1.966317e+02</td>
	90	+ <td>9.991138e-01</td>
	91	+ <td>2.440948e+01</td>
	92	+ <td>-1.000000e+02</td>
	93	+ <td>2.938171e+169</td>
	94	+ </tr>
	95	+ <tr>
	96	+ <th>cleanliness_premium</th>
	97	+ <td>2.072908e-01</td>
	98	+ <td>1.101212e-01</td>
	99	+ <td>5.978325e-02</td>
	100	+ <td>2.303404e+01</td>
	101	+ <td>-8.506287e-01</td>
	102	+ <td>5.267242e+01</td>
	103	+ </tr>
	104	+ <tr>
	105	+ <th>guests_count</th>
	106	+ <td>1.867463e-01</td>
	107	+ <td>2.521722e-01</td>
	108	+ <td>4.589658e-01</td>
	109	+ <td>2.053215e+01</td>
	110	+ <td>-2.647186e+01</td>
	111	+ <td>9.758421e+01</td>
	112	+ </tr>
	113	+ <tr>
	114	+ <th>reputation_score</th>
	115	+ <td>1.029860e-01</td>
	116	+ <td>6.057806e-02</td>
	117	+ <td>8.912083e-02</td>
	118	+ <td>1.084758e+01</td>
	119	+ <td>-1.562156e+00</td>
	120	+ <td>2.482178e+01</td>
	121	+ </tr>
	122	+ <tr>
	123	+ <th>is_entire_home</th>
	124	+ <td>5.532025e-02</td>
	125	+ <td>1.354828e-01</td>
	126	+ <td>6.830392e-01</td>
	127	+ <td>5.687902e+00</td>
	128	+ <td>-1.895949e+01</td>
	129	+ <td>3.783148e+01</td>
	130	+ </tr>
	131	+ <tr>
	132	+ <th>superhost_flag</th>
	133	+ <td>3.891072e-02</td>
	134	+ <td>1.047355e-01</td>
	135	+ <td>7.102545e-01</td>
	136	+ <td>3.967766e+00</td>
	137	+ <td>-1.532646e+01</td>
	138	+ <td>2.765849e+01</td>
	139	+ </tr>
	140	+ <tr>
	141	+ <th>is_superhost</th>
	142	+ <td>3.891072e-02</td>
	143	+ <td>1.047355e-01</td>
	144	+ <td>7.102545e-01</td>
	145	+ <td>3.967766e+00</td>
	146	+ <td>-1.532646e+01</td>
	147	+ <td>2.765849e+01</td>
	148	+ </tr>
	149	+ <tr>
	150	+ <th>bathrooms_sq</th>
	151	+ <td>2.820068e-02</td>
	152	+ <td>1.341078e-02</td>
	153	+ <td>3.548002e-02</td>
	154	+ <td>2.860209e+00</td>
	155	+ <td>1.917875e-01</td>
	156	+ <td>5.599698e+00</td>
	157	+ </tr>
	158	+ <tr>
	159	+ <th>leisure_index</th>
	160	+ <td>2.737976e-02</td>
	161	+ <td>5.611154e-01</td>
	162	+ <td>9.610825e-01</td>
	163	+ <td>2.775803e+00</td>
	164	+ <td>-6.578090e+01</td>
	165	+ <td>2.086833e+02</td>
	166	+ </tr>
	167	+ <tr>
	168	+ <th>pets_allowed</th>
	169	+ <td>2.270908e-02</td>
	170	+ <td>9.276343e-02</td>
	171	+ <td>8.066063e-01</td>
	172	+ <td>2.296890e+00</td>
	173	+ <td>-1.470923e+01</td>
	174	+ <td>2.269385e+01</td>
	175	+ </tr>
	176	+ <tr>
	177	+ <th>bedrooms_per_guest</th>
	178	+ <td>1.758190e-02</td>
	179	+ <td>5.294611e-01</td>
	180	+ <td>9.735094e-01</td>
	181	+ <td>1.773737e+00</td>
	182	+ <td>-6.394566e+01</td>
	183	+ <td>1.872856e+02</td>
	184	+ </tr>
	185	+ <tr>
	186	+ <th>title_length</th>
	187	+ <td>6.894999e-03</td>
	188	+ <td>5.918464e-03</td>
	189	+ <td>2.440198e-01</td>
	190	+ <td>6.918824e-01</td>
	191	+ <td>-4.693926e-01</td>
	192	+ <td>1.866707e+00</td>
	193	+ </tr>
	194	+ <tr>
	195	+ <th>long</th>
	196	+ <td>4.155148e-03</td>
	197	+ <td>1.860213e-02</td>
	198	+ <td>8.232480e-01</td>
	199	+ <td>4.163793e-01</td>
	200	+ <td>-3.178815e+00</td>
	201	+ <td>4.145072e+00</td>
	202	+ </tr>
	203	+ <tr>
	204	+ <th>amenities_count</th>
	205	+ <td>3.040648e-03</td>
	206	+ <td>1.250763e-02</td>
	207	+ <td>8.079252e-01</td>
	208	+ <td>3.045276e-01</td>
	209	+ <td>-2.124493e+00</td>
	210	+ <td>2.793831e+00</td>
	211	+ </tr>
	212	+ <tr>
	213	+ <th>local_density_1km</th>
	214	+ <td>2.991874e-03</td>
	215	+ <td>1.086222e-03</td>
	216	+ <td>5.880233e-03</td>
	217	+ <td>2.996354e-01</td>
	218	+ <td>8.632910e-02</td>
	219	+ <td>5.133963e-01</td>
	220	+ </tr>
	221	+ <tr>
	222	+ <th>num_reviews</th>
	223	+ <td>2.826084e-03</td>
	224	+ <td>1.067144e-03</td>
	225	+ <td>8.090507e-03</td>
	226	+ <td>2.830081e-01</td>
	227	+ <td>7.347904e-02</td>
	228	+ <td>4.929758e-01</td>
	229	+ </tr>
	230	+ <tr>
	231	+ <th>multi_listing_host</th>
	232	+ <td>2.490453e-03</td>
	233	+ <td>7.804680e-02</td>
	234	+ <td>9.745440e-01</td>
	235	+ <td>2.493557e-01</td>
	236	+ <td>-1.397037e+01</td>
	237	+ <td>1.681945e+01</td>
	238	+ </tr>
	239	+ <tr>
	240	+ <th>local_avg_price_500m</th>
	241	+ <td>2.530404e-04</td>
	242	+ <td>3.852892e-05</td>
	243	+ <td>5.115145e-11</td>
	244	+ <td>2.530724e-02</td>
	245	+ <td>1.775409e-02</td>
	246	+ <td>3.286097e-02</td>
	247	+ </tr>
	248	+ <tr>
	249	+ <th>host_professional_dummy</th>
	250	+ <td>3.529386e-15</td>
	251	+ <td>1.448399e-14</td>
	252	+ <td>8.074826e-01</td>
	253	+ <td>3.552714e-13</td>
	254	+ <td>-2.486900e-12</td>
	255	+ <td>3.197442e-12</td>
	256	+ </tr>
	257	+ <tr>
	258	+ <th>cozy_language_score</th>
	259	+ <td>2.333801e-15</td>
	260	+ <td>3.560986e-14</td>
	261	+ <td>9.477456e-01</td>
	262	+ <td>2.442491e-13</td>
	263	+ <td>-6.750156e-12</td>
	264	+ <td>7.216450e-12</td>
	265	+ </tr>
	266	+ <tr>
	267	+ <th>distance_to_center</th>
	268	+ <td>-5.245214e-06</td>
	269	+ <td>2.838915e-06</td>
	270	+ <td>6.465849e-02</td>
	271	+ <td>-5.245200e-04</td>
	272	+ <td>-1.080933e-03</td>
	273	+ <td>3.189577e-05</td>
	274	+ </tr>
	275	+ <tr>
	276	+ <th>local_avg_price_1km</th>
	277	+ <td>-9.066246e-05</td>
	278	+ <td>5.488304e-05</td>
	279	+ <td>9.855058e-02</td>
	280	+ <td>-9.065835e-03</td>
	281	+ <td>-1.982116e-02</td>
	282	+ <td>1.690646e-03</td>
	283	+ </tr>
	284	+ <tr>
	285	+ <th>accommodates_sq</th>
	286	+ <td>-5.362699e-03</td>
	287	+ <td>7.624318e-03</td>
	288	+ <td>4.818266e-01</td>
	289	+ <td>-5.348345e-01</td>
	290	+ <td>-2.010131e+00</td>
	291	+ <td>9.626731e-01</td>
	292	+ </tr>
	293	+ <tr>
	294	+ <th>local_density_500m</th>
	295	+ <td>-7.594815e-03</td>
	296	+ <td>2.870872e-03</td>
	297	+ <td>8.157664e-03</td>
	298	+ <td>-7.566047e-01</td>
	299	+ <td>-1.313460e+00</td>
	300	+ <td>-1.966075e-01</td>
	301	+ </tr>
	302	+ <tr>
	303	+ <th>superhost_x_entire_home</th>
	304	+ <td>-9.461462e-03</td>
	305	+ <td>1.211684e-01</td>
	306	+ <td>9.377603e-01</td>
	307	+ <td>-9.416843e-01</td>
	308	+ <td>-2.188180e+01</td>
	309	+ <td>2.561158e+01</td>
	310	+ </tr>
	311	+ <tr>
	312	+ <th>is_guest_favorite</th>
	313	+ <td>-1.212462e-02</td>
	314	+ <td>1.022872e-01</td>
	315	+ <td>9.056437e-01</td>
	316	+ <td>-1.205142e+00</td>
	317	+ <td>-1.915237e+01</td>
	318	+ <td>2.072616e+01</td>
	319	+ </tr>
	320	+ <tr>
	321	+ <th>guest_favorite_flag</th>
	322	+ <td>-1.212462e-02</td>
	323	+ <td>1.022872e-01</td>
	324	+ <td>9.056437e-01</td>
	325	+ <td>-1.205142e+00</td>
	326	+ <td>-1.915237e+01</td>
	327	+ <td>2.072616e+01</td>
	328	+ </tr>
	329	+ <tr>
	330	+ <th>spatial_cluster</th>
	331	+ <td>-1.339096e-02</td>
	332	+ <td>5.025555e-03</td>
	333	+ <td>7.708607e-03</td>
	334	+ <td>-1.330170e+00</td>
	335	+ <td>-2.297288e+00</td>
	336	+ <td>-3.534791e-01</td>
	337	+ </tr>
	338	+ <tr>
	339	+ <th>quality_score</th>
	340	+ <td>-1.710169e-02</td>
	341	+ <td>1.801382e-02</td>
	342	+ <td>3.424349e-01</td>
	343	+ <td>-1.695629e+00</td>
	344	+ <td>-5.105850e+00</td>
	345	+ <td>1.837146e+00</td>
	346	+ </tr>
	347	+ <tr>
	348	+ <th>text_embed_3</th>
	349	+ <td>-5.468598e-02</td>
	350	+ <td>2.441579e-01</td>
	351	+ <td>8.227745e-01</td>
	352	+ <td>-5.321759e+00</td>
	353	+ <td>-4.132912e+01</td>
	354	+ <td>5.278396e+01</td>
	355	+ </tr>
	356	+ <tr>
	357	+ <th>title_word_count</th>
	358	+ <td>-5.781840e-02</td>
	359	+ <td>5.284647e-02</td>
	360	+ <td>2.739187e-01</td>
	361	+ <td>-5.617867e+00</td>
	362	+ <td>-1.490446e+01</td>
	363	+ <td>4.682186e+00</td>
	364	+ </tr>
	365	+ <tr>
	366	+ <th>bedrooms_sq</th>
	367	+ <td>-6.313440e-02</td>
	368	+ <td>2.009878e-02</td>
	369	+ <td>1.682537e-03</td>
	370	+ <td>-6.118271e+00</td>
	371	+ <td>-9.744647e+00</td>
	372	+ <td>-2.346191e+00</td>
	373	+ </tr>
	374	+ <tr>
	375	+ <th>text_embed_2</th>
	376	+ <td>-8.534285e-02</td>
	377	+ <td>4.600115e-01</td>
	378	+ <td>8.528186e-01</td>
	379	+ <td>-8.180257e+00</td>
	380	+ <td>-6.272879e+01</td>
	381	+ <td>1.262031e+02</td>
	382	+ </tr>
	383	+ <tr>
	384	+ <th>lat</th>
	385	+ <td>-8.973780e-02</td>
	386	+ <td>4.538372e-02</td>
	387	+ <td>4.800632e-02</td>
	388	+ <td>-8.582915e+00</td>
	389	+ <td>-1.636334e+01</td>
	390	+ <td>-7.870313e-02</td>
	391	+ </tr>
	392	+ <tr>
	393	+ <th>remote_work_index</th>
	394	+ <td>-9.000507e-02</td>
	395	+ <td>5.682260e-01</td>
	396	+ <td>8.741443e-01</td>
	397	+ <td>-8.607345e+00</td>
	398	+ <td>-6.999204e+01</td>
	399	+ <td>1.783468e+02</td>
	400	+ </tr>
	401	+ <tr>
	402	+ <th>large_capacity_listing</th>
	403	+ <td>-1.249384e-01</td>
	404	+ <td>1.479728e-01</td>
	405	+ <td>3.984829e-01</td>
	406	+ <td>-1.174488e+01</td>
	407	+ <td>-3.396332e+01</td>
	408	+ <td>1.794909e+01</td>
	409	+ </tr>
	410	+ <tr>
	411	+ <th>location_rating_premium</th>
	412	+ <td>-1.459294e-01</td>
	413	+ <td>1.160389e-01</td>
	414	+ <td>2.085399e-01</td>
	415	+ <td>-1.357813e+01</td>
	416	+ <td>-3.115836e+01</td>
	417	+ <td>8.491608e+00</td>
	418	+ </tr>
	419	+ <tr>
	420	+ <th>bathrooms</th>
	421	+ <td>-1.644262e-01</td>
	422	+ <td>2.263125e-01</td>
	423	+ <td>4.675047e-01</td>
	424	+ <td>-1.516197e+01</td>
	425	+ <td>-4.555562e+01</td>
	426	+ <td>3.219900e+01</td>
	427	+ </tr>
	428	+ <tr>
	429	+ <th>review_quality</th>
	430	+ <td>-1.645432e-01</td>
	431	+ <td>1.351379e-01</td>
	432	+ <td>2.233781e-01</td>
	433	+ <td>-1.517189e+01</td>
	434	+ <td>-3.491061e+01</td>
	435	+ <td>1.055272e+01</td>
	436	+ </tr>
	437	+ <tr>
	438	+ <th>tourism_language_score</th>
	439	+ <td>-2.087261e-01</td>
	440	+ <td>1.149914e-01</td>
	441	+ <td>6.950167e-02</td>
	442	+ <td>-1.883825e+01</td>
	443	+ <td>-3.521558e+01</td>
	444	+ <td>1.679240e+00</td>
	445	+ </tr>
	446	+ <tr>
	447	+ <th>is_private_room</th>
	448	+ <td>-2.634794e-01</td>
	449	+ <td>1.353584e-01</td>
	450	+ <td>5.159093e-02</td>
	451	+ <td>-2.316266e+01</td>
	452	+ <td>-4.106747e+01</td>
	453	+ <td>1.819853e-01</td>
	454	+ </tr>
	455	+ <tr>
	456	+ <th>amenity_richness_index</th>
	457	+ <td>-2.691596e-01</td>
	458	+ <td>1.057993e-01</td>
	459	+ <td>1.095729e-02</td>
	460	+ <td>-2.359787e+01</td>
	461	+ <td>-3.790610e+01</td>
	462	+ <td>-5.992606e+00</td>
	463	+ </tr>
	464	+ <tr>
	465	+ <th>log_reviews</th>
	466	+ <td>-3.999977e-01</td>
	467	+ <td>6.572429e-02</td>
	468	+ <td>1.157707e-09</td>
	469	+ <td>-3.296784e+01</td>
	470	+ <td>-4.106971e+01</td>
	471	+ <td>-2.375211e+01</td>
	472	+ </tr>
	473	+ <tr>
	474	+ <th>bathrooms_per_guest</th>
	475	+ <td>-4.407760e-01</td>
	476	+ <td>3.346925e-01</td>
	477	+ <td>1.878527e-01</td>
	478	+ <td>-3.564631e+01</td>
	479	+ <td>-6.660491e+01</td>
	480	+ <td>2.401215e+01</td>
	481	+ </tr>
	482	+ <tr>
	483	+ <th>professional_tone_score</th>
	484	+ <td>-6.226255e-01</td>
	485	+ <td>1.950698e+01</td>
	486	+ <td>9.745374e-01</td>
	487	+ <td>-4.634661e+01</td>
	488	+ <td>-1.000000e+02</td>
	489	+ <td>2.157594e+18</td>
	490	+ </tr>
	491	+ <tr>
	492	+ <th>log_accommodates</th>
	493	+ <td>-1.094300e+00</td>
	494	+ <td>1.135450e+00</td>
	495	+ <td>3.351667e-01</td>
	496	+ <td>-6.652262e+01</td>
	497	+ <td>-9.638378e+01</td>
	498	+ <td>2.099190e+02</td>
	499	+ </tr>
	500	+ <tr>
	501	+ <th>business_travel_index</th>
	502	+ <td>-1.156153e+00</td>
	503	+ <td>5.602061e-01</td>
	504	+ <td>3.903674e-02</td>
	505	+ <td>-6.853055e+01</td>
	506	+ <td>-8.950359e+01</td>
	507	+ <td>-5.650975e+00</td>
	508	+ </tr>
	509	+ <tr>
	510	+ <th>log_bedrooms</th>
	511	+ <td>-2.053250e+00</td>
	512	+ <td>7.740342e-01</td>
	513	+ <td>7.986024e-03</td>
	514	+ <td>-8.716828e+01</td>
	515	+ <td>-9.718534e+01</td>
	516	+ <td>-4.150158e+01</td>
	517	+ </tr>
	518	+ </tbody>
	519	+</table>
		\ No newline at end of file

added results/tables/price_premiums.tex +69 −0

		`@@ -0,0 +1,69 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Price Premiums}
	4	+\label{tab:price_premiums}
	5	+\begin{tabular}{lccccc}
	6	+\toprule
	7	+ & coefficient & std_error & p_value & premium_pct & premium_lo_95 & premium_hi_95 \\
	8	+\midrule
	9	+has_reviews & 9.5113 & 3.7385 & 0.0110 & 1351079.5 & 788.2 & 2055398517.7 \\
	10	+text_embed_0 & 2.8001 & 0.4300 & 0.0000 & 1544.7 & 608.1 & 3720.4 \\
	11	+bedrooms & 1.1407 & 0.3107 & 0.0002 & 212.9 & 70.1786 & 475.3 \\
	12	+rating & 1.0516 & 0.4938 & 0.0332 & 186.2 & 8.7314 & 653.4 \\
	13	+family_comfort_index & 0.9212 & 0.5881 & 0.1173 & 151.2 & -20.6642 & 695.6 \\
	14	+log_bathrooms & 0.6445 & 0.5406 & 0.2332 & 90.5067 & -33.9668 & 449.6 \\
	15	+text_embed_1 & 0.4787 & 0.4423 & 0.2792 & 61.3951 & -32.1757 & 284.1 \\
	16	+luxury_index & 0.4454 & 0.5705 & 0.4350 & 56.1069 & -48.9721 & 377.6 \\
	17	+luxury_language_score & 0.2184 & 196.6 & 0.9991 & 24.4095 & -100.0 & 29381714283305050664764789594281384348919858297851914379391959376445857898484237145821157151224622002690364052532378072219128359490199350405697171288874777972417088192512.0 \\
	18	+cleanliness_premium & 0.2073 & 0.1101 & 0.0598 & 23.0340 & -0.8506 & 52.6724 \\
	19	+guests_count & 0.1867 & 0.2522 & 0.4590 & 20.5321 & -26.4719 & 97.5842 \\
	20	+reputation_score & 0.1030 & 0.0606 & 0.0891 & 10.8476 & -1.5622 & 24.8218 \\
	21	+is_entire_home & 0.0553 & 0.1355 & 0.6830 & 5.6879 & -18.9595 & 37.8315 \\
	22	+superhost_flag & 0.0389 & 0.1047 & 0.7103 & 3.9678 & -15.3265 & 27.6585 \\
	23	+is_superhost & 0.0389 & 0.1047 & 0.7103 & 3.9678 & -15.3265 & 27.6585 \\
	24	+bathrooms_sq & 0.0282 & 0.0134 & 0.0355 & 2.8602 & 0.1918 & 5.5997 \\
	25	+leisure_index & 0.0274 & 0.5611 & 0.9611 & 2.7758 & -65.7809 & 208.7 \\
	26	+pets_allowed & 0.0227 & 0.0928 & 0.8066 & 2.2969 & -14.7092 & 22.6939 \\
	27	+bedrooms_per_guest & 0.0176 & 0.5295 & 0.9735 & 1.7737 & -63.9457 & 187.3 \\
	28	+title_length & 0.0069 & 0.0059 & 0.2440 & 0.6919 & -0.4694 & 1.8667 \\
	29	+long & 0.0042 & 0.0186 & 0.8232 & 0.4164 & -3.1788 & 4.1451 \\
	30	+amenities_count & 0.0030 & 0.0125 & 0.8079 & 0.3045 & -2.1245 & 2.7938 \\
	31	+local_density_1km & 0.0030 & 0.0011 & 0.0059 & 0.2996 & 0.0863 & 0.5134 \\
	32	+num_reviews & 0.0028 & 0.0011 & 0.0081 & 0.2830 & 0.0735 & 0.4930 \\
	33	+multi_listing_host & 0.0025 & 0.0780 & 0.9745 & 0.2494 & -13.9704 & 16.8194 \\
	34	+local_avg_price_500m & 0.0003 & 0.0000 & 0.0000 & 0.0253 & 0.0178 & 0.0329 \\
	35	+host_professional_dummy & 0.0000 & 0.0000 & 0.8075 & 0.0000 & -0.0000 & 0.0000 \\
	36	+cozy_language_score & 0.0000 & 0.0000 & 0.9477 & 0.0000 & -0.0000 & 0.0000 \\
	37	+distance_to_center & -0.0000 & 0.0000 & 0.0647 & -0.0005 & -0.0011 & 0.0000 \\
	38	+local_avg_price_1km & -0.0001 & 0.0001 & 0.0986 & -0.0091 & -0.0198 & 0.0017 \\
	39	+accommodates_sq & -0.0054 & 0.0076 & 0.4818 & -0.5348 & -2.0101 & 0.9627 \\
	40	+local_density_500m & -0.0076 & 0.0029 & 0.0082 & -0.7566 & -1.3135 & -0.1966 \\
	41	+superhost_x_entire_home & -0.0095 & 0.1212 & 0.9378 & -0.9417 & -21.8818 & 25.6116 \\
	42	+is_guest_favorite & -0.0121 & 0.1023 & 0.9056 & -1.2051 & -19.1524 & 20.7262 \\
	43	+guest_favorite_flag & -0.0121 & 0.1023 & 0.9056 & -1.2051 & -19.1524 & 20.7262 \\
	44	+spatial_cluster & -0.0134 & 0.0050 & 0.0077 & -1.3302 & -2.2973 & -0.3535 \\
	45	+quality_score & -0.0171 & 0.0180 & 0.3424 & -1.6956 & -5.1059 & 1.8371 \\
	46	+text_embed_3 & -0.0547 & 0.2442 & 0.8228 & -5.3218 & -41.3291 & 52.7840 \\
	47	+title_word_count & -0.0578 & 0.0528 & 0.2739 & -5.6179 & -14.9045 & 4.6822 \\
	48	+bedrooms_sq & -0.0631 & 0.0201 & 0.0017 & -6.1183 & -9.7446 & -2.3462 \\
	49	+text_embed_2 & -0.0853 & 0.4600 & 0.8528 & -8.1803 & -62.7288 & 126.2 \\
	50	+lat & -0.0897 & 0.0454 & 0.0480 & -8.5829 & -16.3633 & -0.0787 \\
	51	+remote_work_index & -0.0900 & 0.5682 & 0.8741 & -8.6073 & -69.9920 & 178.3 \\
	52	+large_capacity_listing & -0.1249 & 0.1480 & 0.3985 & -11.7449 & -33.9633 & 17.9491 \\
	53	+location_rating_premium & -0.1459 & 0.1160 & 0.2085 & -13.5781 & -31.1584 & 8.4916 \\
	54	+bathrooms & -0.1644 & 0.2263 & 0.4675 & -15.1620 & -45.5556 & 32.1990 \\
	55	+review_quality & -0.1645 & 0.1351 & 0.2234 & -15.1719 & -34.9106 & 10.5527 \\
	56	+tourism_language_score & -0.2087 & 0.1150 & 0.0695 & -18.8382 & -35.2156 & 1.6792 \\
	57	+is_private_room & -0.2635 & 0.1354 & 0.0516 & -23.1627 & -41.0675 & 0.1820 \\
	58	+amenity_richness_index & -0.2692 & 0.1058 & 0.0110 & -23.5979 & -37.9061 & -5.9926 \\
	59	+log_reviews & -0.4000 & 0.0657 & 0.0000 & -32.9678 & -41.0697 & -23.7521 \\
	60	+bathrooms_per_guest & -0.4408 & 0.3347 & 0.1879 & -35.6463 & -66.6049 & 24.0121 \\
	61	+professional_tone_score & -0.6226 & 19.5070 & 0.9745 & -46.3466 & -100.0 & 2157593613751480064.0 \\
	62	+log_accommodates & -1.0943 & 1.1354 & 0.3352 & -66.5226 & -96.3838 & 209.9 \\
	63	+business_travel_index & -1.1562 & 0.5602 & 0.0390 & -68.5306 & -89.5036 & -5.6510 \\
	64	+log_bedrooms & -2.0532 & 0.7740 & 0.0080 & -87.1683 & -97.1853 & -41.5016 \\
	65	+\bottomrule
	66	+\end{tabular}
	67	+\par\smallskip\footnotesize
	68	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	69	+\end{table}
		\ No newline at end of file

added results/tables/quantile_coefficients.csv +81 −0

		`@@ -0,0 +1,81 @@`
	1	+tau,variable,coefficient,std_error,p_value,ci_lower,ci_upper
	2	+0.1,const,3.037136041732424,0.1256830472721875,3.453861896553987e-119,2.790714020831887,3.2835580626329612
	3	+0.1,log_accommodates,-0.14598843590692923,0.14486450773374018,0.3136421783128215,-0.4300188244699965,0.13804195265613806
	4	+0.1,log_bedrooms,0.1544597794818401,0.07437505631451387,0.03789789964870194,0.00863540555556025,0.3002841534081199
	5	+0.1,log_beds,0.15445977935632982,0.0743750563143857,0.03789789980430119,0.00863540543030128,0.30028415328235836
	6	+0.1,log_bathrooms,0.7955570577687467,0.1225738572248126,9.802057550891489e-11,0.5552311087845649,1.0358830067529285
	7	+0.1,bathrooms_per_guest,-0.4981871543204761,0.12061125102799895,3.706612426421392e-05,-0.7346650951941976,-0.2617092134467546
	8	+0.1,bedrooms_per_guest,-0.2532290279632434,0.16989823372047097,0.13619226527698927,-0.5863421009631449,0.07988404503665808
	9	+0.1,is_entire_home,1.006682099889531,0.04977307514136882,5.554204952019437e-86,0.9090939045921325,1.1042702951869294
	10	+0.1,is_private_room,1.018041577661279,0.04658994749692913,3.761571758912735e-99,0.9266944209442745,1.1093887343782836
	11	+0.1,is_shared_room,1.0124123642026461,0.051692861754800784,5.17672223698994e-81,0.9110601155404754,1.1137646128648169
	12	+0.1,large_capacity_listing,0.1193801853532932,0.05931760577085832,0.04423978909490329,0.0030783880442226824,0.23568198266236373
	13	+0.1,accommodates_sq,-0.0018999301268678437,0.0016811729143116308,0.25850467775465574,-0.005196142591630335,0.0013962823378946476
	14	+0.1,bedrooms_sq,0.0022516844660458446,0.006590421285508101,0.7326277756615873,-0.010669906528193239,0.015173275460284928
	15	+0.1,bathrooms_sq,-0.004178465178256374,0.007132196288712377,0.558008822883833,-0.018162294032418324,0.009805363675905576
	16	+0.1,accommodates_bedrooms,0.004187224053794125,0.006122068382413697,0.4940497282613679,-0.007816085029080957,0.016190533136669207
	17	+0.1,accommodates_bathrooms,0.003909201521423711,0.006979612632877434,0.5754559213371264,-0.009775462300991277,0.0175938653438387
	18	+0.25,const,3.208674486192592,0.12284791439006339,3.3390868933749757e-137,2.9678112036382185,3.449537768746966
	19	+0.25,log_accommodates,-0.03580456050349312,0.13862096948399757,0.7961979093003081,-0.30759347858614305,0.2359843575791568
	20	+0.25,log_bedrooms,0.1292361991040707,0.07775215999956565,0.09657378734658319,-0.023209534925444558,0.2816819331335859
	21	+0.25,log_beds,0.12923619903631334,0.07775215999943505,0.0965737875207237,-0.02320953499294584,0.28168193306557254
	22	+0.25,log_bathrooms,0.5416739773659174,0.11628402568468124,3.3121993326613764e-06,0.31368026437444213,0.7696676903573927
	23	+0.25,bathrooms_per_guest,-0.21462389809084925,0.1196407394464072,0.07291750596775766,-0.44919899343023806,0.019951197248539526
	24	+0.25,bedrooms_per_guest,-0.1508080694175078,0.18528731489522665,0.4157508491206335,-0.5140939347326134,0.2124777958975979
	25	+0.25,is_entire_home,1.066097433444014,0.04758150548119737,8.078969291545103e-104,0.9728061663029977,1.1593887005850305
	26	+0.25,is_private_room,1.1027973354383107,0.04472074110855588,1.1737183594910301e-123,1.0151150613423139,1.1904796095343075
	27	+0.25,is_shared_room,1.0397797173075105,0.04963462768871822,9.594242517773428e-92,0.9424629707202694,1.1370964638947516
	28	+0.25,large_capacity_listing,0.06497644997614316,0.055079612332733244,0.23820913458968265,-0.04301607308908828,0.1729689730413746
	29	+0.25,accommodates_sq,-0.0004201009906878994,0.0014925548876136075,0.7783714065691141,-0.0033464971888497006,0.0025062952074739017
	30	+0.25,bedrooms_sq,0.001159323717075722,0.006516642442037284,0.8588105451987466,-0.011617611875322176,0.01393625930947362
	31	+0.25,bathrooms_sq,-0.005725049265123516,0.005839115201388641,0.3269263388694107,-0.017173582694465735,0.0057234841642187
	32	+0.25,accommodates_bedrooms,0.002567577160220935,0.006405602506649917,0.688568248790647,-0.009991646609678247,0.015126800930120117
	33	+0.25,accommodates_bathrooms,0.007200219740298053,0.005853069471178083,0.2187218534307975,-0.004275673300673079,0.018676112781269183
	34	+0.5,const,3.6432785232045717,0.13558099908019852,2.1734280823954096e-144,3.3774499606880917,3.9091070857210517
	35	+0.5,log_accommodates,-0.1239666162928188,0.15843506427648682,0.43400801661999244,-0.4346042843624568,0.1866710517768192
	36	+0.5,log_bedrooms,-0.22368354497143628,0.08359042112964153,0.007487600433820289,-0.38757613788481243,-0.05979095205806012
	37	+0.5,log_beds,-0.22368354472610008,0.08359042112951881,0.007487600499161293,-0.3875761376392356,-0.05979095181296454
	38	+0.5,log_bathrooms,0.5187170660196898,0.1591886502909967,0.001131033762238574,0.20660187021144005,0.8308322618279396
	39	+0.5,bathrooms_per_guest,0.10247069691308752,0.1713940002284706,0.54996898928511,-0.2335750691882023,0.43851646301437736
	40	+0.5,bedrooms_per_guest,1.240469447816622,0.17699303833903193,2.89081193856308e-12,0.8934458584030471,1.587493037230197
	41	+0.5,is_entire_home,1.1771217233347784,0.055149362102980444,5.814507735308219e-95,1.0689924445199475,1.2852510021496093
	42	+0.5,is_private_room,1.3821255075399677,0.05005395969879054,1.3527925648148004e-151,1.283986592461486,1.4802644226184494
	43	+0.5,is_shared_room,1.084031292411737,0.05640048123335032,3.0866023814781237e-78,0.9734489914019128,1.1946135934215614
	44	+0.5,large_capacity_listing,0.20371889974362034,0.06951852071953536,0.003407531051302581,0.0674165523902499,0.3400212470969908
	45	+0.5,accommodates_sq,0.0015304497648145343,0.0018121408796986553,0.3984196854785001,-0.0020225466609065804,0.005083446190535649
	46	+0.5,bedrooms_sq,-0.012259508793121654,0.005527388091226047,0.026623656429000477,-0.023096850608933114,-0.0014221669773101953
	47	+0.5,bathrooms_sq,-0.0008318633388739727,0.006647998628509719,0.9004281222468478,-0.01386634406270256,0.012202617384954614
	48	+0.5,accommodates_bedrooms,0.015404275927721756,0.006436227785826918,0.016748329565245357,0.002785006325474985,0.028023545529968524
	49	+0.5,accommodates_bathrooms,0.0017216115353448913,0.007343282822431821,0.814652003497577,-0.012676086741567325,0.016119309812257107
	50	+0.75,const,6.589242832091713,0.2714306626976802,3.479818058797797e-120,6.057058946611274,7.121426717572152
	51	+0.75,log_accommodates,-2.128506744165861,0.32809358970827673,9.992235439466934e-11,-2.7717874982148323,-1.48522599011689
	52	+0.75,log_bedrooms,0.52813352818157,0.17221895558743364,0.0021818298430818115,0.19047030315387342,0.8657967532092665
	53	+0.75,log_beds,0.5281335282629698,0.17221895558718692,0.0021818298396098427,0.19047030323575692,0.8657967532901827
	54	+0.75,log_bathrooms,0.6777635135222226,0.33107780227984546,0.04072082624921829,0.028631726190376128,1.3268953008540691
	55	+0.75,bathrooms_per_guest,-0.301276972715641,0.3459354873100249,0.383867506193232,-0.9795396639402887,0.37698571850900664
	56	+0.75,bedrooms_per_guest,-0.8978081729028418,0.3439421642305978,0.009084778719532883,-1.5721626305812713,-0.22345371522441237
	57	+0.75,is_entire_home,1.8747240591029595,0.11402326830762732,1.653927039912284e-58,1.6511629280205855,2.0982851901853334
	58	+0.75,is_private_room,2.792806779230318,0.09862275394475796,1.4403816625163647e-158,2.599440857084813,2.9861727013758235
	59	+0.75,is_shared_room,1.9217119938216456,0.11062234701414758,7.883410150844573e-65,1.7048189211310132,2.138605066512278
	60	+0.75,large_capacity_listing,0.11752285411785124,0.13434716602340183,0.3817609533593507,-0.14588657835755592,0.3809322865932584
	61	+0.75,accommodates_sq,0.010324122726078855,0.0037815229955525833,0.00636321039016242,0.002909832900436387,0.017738412551721323
	62	+0.75,bedrooms_sq,0.036257560461251614,0.009201509364055725,8.298457295620307e-05,0.01821650733564367,0.054298613586859557
	63	+0.75,bathrooms_sq,-0.0185077663408606,0.014688281304817625,0.2077428269481931,-0.04730652672459061,0.010290994042869414
	64	+0.75,accommodates_bedrooms,-0.028955293503429402,0.012527761218920816,0.020876378224955288,-0.05351800346255526,-0.004392583544303545
	65	+0.75,accommodates_bathrooms,0.010364880646449137,0.015677139564204603,0.5085634074695655,-0.020372696912318054,0.041102458205216325
	66	+0.9,const,5.590230457306461,0.21774402580456434,5.1996454892313496e-133,5.16330794036926,6.017152974243662
	67	+0.9,log_accommodates,0.25185928713108297,0.2690341284189734,0.34925677180472914,-0.2756258037789364,0.7793443780411023
	68	+0.9,log_bedrooms,0.4124116072862307,0.14069393896635718,0.003398337266151473,0.13655829595250119,0.6882649186199603
	69	+0.9,log_beds,0.412411607327158,0.14069393896616572,0.003398337262933627,0.1365582959938038,0.6882649186605121
	70	+0.9,log_bathrooms,-0.31936915437654534,0.2845618233195369,0.2618061066168788,-0.8772988123174483,0.23856050356435765
	71	+0.9,bathrooms_per_guest,0.5189901505000307,0.3072318983488105,0.0912641034255634,-0.08338787039774842,1.1213681713978099
	72	+0.9,bedrooms_per_guest,-0.8020189134203974,0.2523539669744513,0.0014954808948569596,-1.29679983951808,-0.307237987322715
	73	+0.9,is_entire_home,1.8177282059045865,0.08988906564897185,5.9204921291713495e-86,1.641486097721851,1.993970314087322
	74	+0.9,is_private_room,1.7910242001133838,0.07918994573309662,1.2818238426806459e-105,1.6357594537049647,1.946288946521803
	75	+0.9,is_shared_room,1.9814780513124788,0.09003847025883953,1.8701291104187626e-100,1.804943011133786,2.1580130914911715
	76	+0.9,large_capacity_listing,0.15947090848885637,0.10856530410126221,0.14195414753921243,-0.053388997576093,0.37233081455380573
	77	+0.9,accommodates_sq,-0.004725315800804708,0.003351246580570982,0.15862608520928959,-0.011295978852376525,0.00184534725076711
	78	+0.9,bedrooms_sq,0.05482543794676076,0.007013285915827532,7.139424622267303e-15,0.04107475218570715,0.06857612370781437
	79	+0.9,bathrooms_sq,-0.047089974682535285,0.016458888322815488,0.004247974184682172,-0.07936029761241196,-0.014819651752658605
	80	+0.9,accommodates_bedrooms,-0.04863634474338596,0.011903779934832692,4.4941825121379705e-05,-0.07197563808136553,-0.025297051405406386
	81	+0.9,accommodates_bathrooms,0.045980835835166545,0.015564171559041096,0.0031555629675864604,0.015464750392748236,0.07649692127758485

added results/tables/robustness_summary.csv +27 −0

		`@@ -0,0 +1,27 @@`
	1	+,Category,Specification,N,R2,Adj_R2,AIC,Vars (total),Vars (p<.05)
	2	+0,Winsorization,Raw OLS,2861,0.0937,0.0911,9645.0,9,7
	3	+1,Winsorization,Winsorized (1%/99%),2861,0.0948,0.0923,9576.5,9,7
	4	+2,Winsorization,Trimmed (1%-99%),2804,0.0999,0.0973,9169.3,9,8
	5	+3,Winsorization,Log(price) OLS,2861,0.1005,0.098,-760.3,9,7
	6	+4,Winsorization,Median Regression,2861,,,,9,7
	7	+5,Room Type,Full Sample,2861,0.0937,0.0911,9645.0,9,7
	8	+6,Room Type,Apartment,71,0.1858,0.0953,255.7,8,1
	9	+7,Room Type,Cabin/Chalet,609,0.1004,0.0899,1856.8,8,3
	10	+8,Room Type,Condo,220,0.1815,0.1544,779.2,8,2
	11	+9,Room Type,House,534,0.1332,0.1217,1751.7,8,3
	12	+10,Room Type,Other,476,0.1511,0.1384,1625.4,8,3
	13	+11,Room Type,Rental unit,951,0.1258,0.1193,3237.8,8,5
	14	+12,Price Segment,Full Sample,2861,0.0937,0.0911,9645.0,9,7
	15	+13,Price Segment,Budget (<$100),455,0.1116,0.0956,169.7,9,3
	16	+14,Price Segment,Luxury ($400-1000),226,0.1578,0.1268,7.9,9,3
	17	+15,Price Segment,Moderate ($100-200),879,0.0991,0.0908,-466.4,9,5
	18	+16,Price Segment,Premium ($200-400),616,0.0827,0.0706,-360.6,9,2
	19	+17,Price Segment,Ultra-Luxury (>$1000),685,0.1518,0.1418,1240.6,9,4
	20	+18,Location Controls,No location,2861,0.0937,0.0911,9645.0,9,7
	21	+19,Location Controls,city FE,2861,0.164,0.1501,9491.8,48,22
	22	+20,Location Controls,Coordinates,2857,0.1137,0.1106,9571.7,11,8
	23	+21,Location Controls,Spatial clusters,2857,0.1483,0.1402,9492.0,28,10
	24	+22,Location Controls,Spatial lag,2857,0.153,0.1503,9440.3,10,8
	25	+23,Host Type,All hosts,2861,0.082,0.0797,9679.7,8,5
	26	+24,Host Type,Superhosts,1645,0.0769,0.0729,5225.6,8,5
	27	+25,Host Type,Non-superhosts,1216,0.0841,0.0787,4356.0,8,4

added results/tables/robustness_summary.html +303 −0

		`@@ -0,0 +1,303 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>Category</th>
	6	+ <th>Specification</th>
	7	+ <th>N</th>
	8	+ <th>R2</th>
	9	+ <th>Adj_R2</th>
	10	+ <th>AIC</th>
	11	+ <th>Vars (total)</th>
	12	+ <th>Vars (p<.05)</th>
	13	+ </tr>
	14	+ </thead>
	15	+ <tbody>
	16	+ <tr>
	17	+ <th>0</th>
	18	+ <td>Winsorization</td>
	19	+ <td>Raw OLS</td>
	20	+ <td>2861</td>
	21	+ <td>0.0937</td>
	22	+ <td>0.0911</td>
	23	+ <td>9645.0</td>
	24	+ <td>9</td>
	25	+ <td>7</td>
	26	+ </tr>
	27	+ <tr>
	28	+ <th>1</th>
	29	+ <td>Winsorization</td>
	30	+ <td>Winsorized (1%/99%)</td>
	31	+ <td>2861</td>
	32	+ <td>0.0948</td>
	33	+ <td>0.0923</td>
	34	+ <td>9576.5</td>
	35	+ <td>9</td>
	36	+ <td>7</td>
	37	+ </tr>
	38	+ <tr>
	39	+ <th>2</th>
	40	+ <td>Winsorization</td>
	41	+ <td>Trimmed (1%-99%)</td>
	42	+ <td>2804</td>
	43	+ <td>0.0999</td>
	44	+ <td>0.0973</td>
	45	+ <td>9169.3</td>
	46	+ <td>9</td>
	47	+ <td>8</td>
	48	+ </tr>
	49	+ <tr>
	50	+ <th>3</th>
	51	+ <td>Winsorization</td>
	52	+ <td>Log(price) OLS</td>
	53	+ <td>2861</td>
	54	+ <td>0.1005</td>
	55	+ <td>0.0980</td>
	56	+ <td>-760.3</td>
	57	+ <td>9</td>
	58	+ <td>7</td>
	59	+ </tr>
	60	+ <tr>
	61	+ <th>4</th>
	62	+ <td>Winsorization</td>
	63	+ <td>Median Regression</td>
	64	+ <td>2861</td>
	65	+ <td>NaN</td>
	66	+ <td>NaN</td>
	67	+ <td></td>
	68	+ <td>9</td>
	69	+ <td>7</td>
	70	+ </tr>
	71	+ <tr>
	72	+ <th>5</th>
	73	+ <td>Room Type</td>
	74	+ <td>Full Sample</td>
	75	+ <td>2861</td>
	76	+ <td>0.0937</td>
	77	+ <td>0.0911</td>
	78	+ <td>9645.0</td>
	79	+ <td>9</td>
	80	+ <td>7</td>
	81	+ </tr>
	82	+ <tr>
	83	+ <th>6</th>
	84	+ <td>Room Type</td>
	85	+ <td>Apartment</td>
	86	+ <td>71</td>
	87	+ <td>0.1858</td>
	88	+ <td>0.0953</td>
	89	+ <td>255.7</td>
	90	+ <td>8</td>
	91	+ <td>1</td>
	92	+ </tr>
	93	+ <tr>
	94	+ <th>7</th>
	95	+ <td>Room Type</td>
	96	+ <td>Cabin/Chalet</td>
	97	+ <td>609</td>
	98	+ <td>0.1004</td>
	99	+ <td>0.0899</td>
	100	+ <td>1856.8</td>
	101	+ <td>8</td>
	102	+ <td>3</td>
	103	+ </tr>
	104	+ <tr>
	105	+ <th>8</th>
	106	+ <td>Room Type</td>
	107	+ <td>Condo</td>
	108	+ <td>220</td>
	109	+ <td>0.1815</td>
	110	+ <td>0.1544</td>
	111	+ <td>779.2</td>
	112	+ <td>8</td>
	113	+ <td>2</td>
	114	+ </tr>
	115	+ <tr>
	116	+ <th>9</th>
	117	+ <td>Room Type</td>
	118	+ <td>House</td>
	119	+ <td>534</td>
	120	+ <td>0.1332</td>
	121	+ <td>0.1217</td>
	122	+ <td>1751.7</td>
	123	+ <td>8</td>
	124	+ <td>3</td>
	125	+ </tr>
	126	+ <tr>
	127	+ <th>10</th>
	128	+ <td>Room Type</td>
	129	+ <td>Other</td>
	130	+ <td>476</td>
	131	+ <td>0.1511</td>
	132	+ <td>0.1384</td>
	133	+ <td>1625.4</td>
	134	+ <td>8</td>
	135	+ <td>3</td>
	136	+ </tr>
	137	+ <tr>
	138	+ <th>11</th>
	139	+ <td>Room Type</td>
	140	+ <td>Rental unit</td>
	141	+ <td>951</td>
	142	+ <td>0.1258</td>
	143	+ <td>0.1193</td>
	144	+ <td>3237.8</td>
	145	+ <td>8</td>
	146	+ <td>5</td>
	147	+ </tr>
	148	+ <tr>
	149	+ <th>12</th>
	150	+ <td>Price Segment</td>
	151	+ <td>Full Sample</td>
	152	+ <td>2861</td>
	153	+ <td>0.0937</td>
	154	+ <td>0.0911</td>
	155	+ <td>9645.0</td>
	156	+ <td>9</td>
	157	+ <td>7</td>
	158	+ </tr>
	159	+ <tr>
	160	+ <th>13</th>
	161	+ <td>Price Segment</td>
	162	+ <td>Budget (<$100)</td>
	163	+ <td>455</td>
	164	+ <td>0.1116</td>
	165	+ <td>0.0956</td>
	166	+ <td>169.7</td>
	167	+ <td>9</td>
	168	+ <td>3</td>
	169	+ </tr>
	170	+ <tr>
	171	+ <th>14</th>
	172	+ <td>Price Segment</td>
	173	+ <td>Luxury ($400-1000)</td>
	174	+ <td>226</td>
	175	+ <td>0.1578</td>
	176	+ <td>0.1268</td>
	177	+ <td>7.9</td>
	178	+ <td>9</td>
	179	+ <td>3</td>
	180	+ </tr>
	181	+ <tr>
	182	+ <th>15</th>
	183	+ <td>Price Segment</td>
	184	+ <td>Moderate ($100-200)</td>
	185	+ <td>879</td>
	186	+ <td>0.0991</td>
	187	+ <td>0.0908</td>
	188	+ <td>-466.4</td>
	189	+ <td>9</td>
	190	+ <td>5</td>
	191	+ </tr>
	192	+ <tr>
	193	+ <th>16</th>
	194	+ <td>Price Segment</td>
	195	+ <td>Premium ($200-400)</td>
	196	+ <td>616</td>
	197	+ <td>0.0827</td>
	198	+ <td>0.0706</td>
	199	+ <td>-360.6</td>
	200	+ <td>9</td>
	201	+ <td>2</td>
	202	+ </tr>
	203	+ <tr>
	204	+ <th>17</th>
	205	+ <td>Price Segment</td>
	206	+ <td>Ultra-Luxury (>$1000)</td>
	207	+ <td>685</td>
	208	+ <td>0.1518</td>
	209	+ <td>0.1418</td>
	210	+ <td>1240.6</td>
	211	+ <td>9</td>
	212	+ <td>4</td>
	213	+ </tr>
	214	+ <tr>
	215	+ <th>18</th>
	216	+ <td>Location Controls</td>
	217	+ <td>No location</td>
	218	+ <td>2861</td>
	219	+ <td>0.0937</td>
	220	+ <td>0.0911</td>
	221	+ <td>9645.0</td>
	222	+ <td>9</td>
	223	+ <td>7</td>
	224	+ </tr>
	225	+ <tr>
	226	+ <th>19</th>
	227	+ <td>Location Controls</td>
	228	+ <td>city FE</td>
	229	+ <td>2861</td>
	230	+ <td>0.1640</td>
	231	+ <td>0.1501</td>
	232	+ <td>9491.8</td>
	233	+ <td>48</td>
	234	+ <td>22</td>
	235	+ </tr>
	236	+ <tr>
	237	+ <th>20</th>
	238	+ <td>Location Controls</td>
	239	+ <td>Coordinates</td>
	240	+ <td>2857</td>
	241	+ <td>0.1137</td>
	242	+ <td>0.1106</td>
	243	+ <td>9571.7</td>
	244	+ <td>11</td>
	245	+ <td>8</td>
	246	+ </tr>
	247	+ <tr>
	248	+ <th>21</th>
	249	+ <td>Location Controls</td>
	250	+ <td>Spatial clusters</td>
	251	+ <td>2857</td>
	252	+ <td>0.1483</td>
	253	+ <td>0.1402</td>
	254	+ <td>9492.0</td>
	255	+ <td>28</td>
	256	+ <td>10</td>
	257	+ </tr>
	258	+ <tr>
	259	+ <th>22</th>
	260	+ <td>Location Controls</td>
	261	+ <td>Spatial lag</td>
	262	+ <td>2857</td>
	263	+ <td>0.1530</td>
	264	+ <td>0.1503</td>
	265	+ <td>9440.3</td>
	266	+ <td>10</td>
	267	+ <td>8</td>
	268	+ </tr>
	269	+ <tr>
	270	+ <th>23</th>
	271	+ <td>Host Type</td>
	272	+ <td>All hosts</td>
	273	+ <td>2861</td>
	274	+ <td>0.0820</td>
	275	+ <td>0.0797</td>
	276	+ <td>9679.7</td>
	277	+ <td>8</td>
	278	+ <td>5</td>
	279	+ </tr>
	280	+ <tr>
	281	+ <th>24</th>
	282	+ <td>Host Type</td>
	283	+ <td>Superhosts</td>
	284	+ <td>1645</td>
	285	+ <td>0.0769</td>
	286	+ <td>0.0729</td>
	287	+ <td>5225.6</td>
	288	+ <td>8</td>
	289	+ <td>5</td>
	290	+ </tr>
	291	+ <tr>
	292	+ <th>25</th>
	293	+ <td>Host Type</td>
	294	+ <td>Non-superhosts</td>
	295	+ <td>1216</td>
	296	+ <td>0.0841</td>
	297	+ <td>0.0787</td>
	298	+ <td>4356.0</td>
	299	+ <td>8</td>
	300	+ <td>4</td>
	301	+ </tr>
	302	+ </tbody>
	303	+</table>
		\ No newline at end of file

added results/tables/robustness_summary.tex +39 −0

		`@@ -0,0 +1,39 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Robustness Summary}
	4	+\label{tab:robustness_summary}
	5	+\begin{tabular}{lccccccc}
	6	+\toprule
	7	+ & Category & Specification & N & R2 & Adj_R2 & AIC & Vars (total) & Vars (p<.05) \\
	8	+\midrule
	9	+0 & Winsorization & Raw OLS & 2861 & 0.0937 & 0.0911 & 9645.0 & 9 & 7 \\
	10	+1 & Winsorization & Winsorized (1%/99%) & 2861 & 0.0948 & 0.0923 & 9576.5 & 9 & 7 \\
	11	+2 & Winsorization & Trimmed (1%-99%) & 2804 & 0.0999 & 0.0973 & 9169.3 & 9 & 8 \\
	12	+3 & Winsorization & Log(price) OLS & 2861 & 0.1005 & 0.0980 & -760.3 & 9 & 7 \\
	13	+4 & Winsorization & Median Regression & 2861 & nan & nan & & 9 & 7 \\
	14	+5 & Room Type & Full Sample & 2861 & 0.0937 & 0.0911 & 9645.0 & 9 & 7 \\
	15	+6 & Room Type & Apartment & 71 & 0.1858 & 0.0953 & 255.7 & 8 & 1 \\
	16	+7 & Room Type & Cabin/Chalet & 609 & 0.1004 & 0.0899 & 1856.8 & 8 & 3 \\
	17	+8 & Room Type & Condo & 220 & 0.1815 & 0.1544 & 779.2 & 8 & 2 \\
	18	+9 & Room Type & House & 534 & 0.1332 & 0.1217 & 1751.7 & 8 & 3 \\
	19	+10 & Room Type & Other & 476 & 0.1511 & 0.1384 & 1625.4 & 8 & 3 \\
	20	+11 & Room Type & Rental unit & 951 & 0.1258 & 0.1193 & 3237.8 & 8 & 5 \\
	21	+12 & Price Segment & Full Sample & 2861 & 0.0937 & 0.0911 & 9645.0 & 9 & 7 \\
	22	+13 & Price Segment & Budget (<$100) & 455 & 0.1116 & 0.0956 & 169.7 & 9 & 3 \\
	23	+14 & Price Segment & Luxury ($400-1000) & 226 & 0.1578 & 0.1268 & 7.9000 & 9 & 3 \\
	24	+15 & Price Segment & Moderate ($100-200) & 879 & 0.0991 & 0.0908 & -466.4 & 9 & 5 \\
	25	+16 & Price Segment & Premium ($200-400) & 616 & 0.0827 & 0.0706 & -360.6 & 9 & 2 \\
	26	+17 & Price Segment & Ultra-Luxury (>$1000) & 685 & 0.1518 & 0.1418 & 1240.6 & 9 & 4 \\
	27	+18 & Location Controls & No location & 2861 & 0.0937 & 0.0911 & 9645.0 & 9 & 7 \\
	28	+19 & Location Controls & city FE & 2861 & 0.1640 & 0.1501 & 9491.8 & 48 & 22 \\
	29	+20 & Location Controls & Coordinates & 2857 & 0.1137 & 0.1106 & 9571.7 & 11 & 8 \\
	30	+21 & Location Controls & Spatial clusters & 2857 & 0.1483 & 0.1402 & 9492.0 & 28 & 10 \\
	31	+22 & Location Controls & Spatial lag & 2857 & 0.1530 & 0.1503 & 9440.3 & 10 & 8 \\
	32	+23 & Host Type & All hosts & 2861 & 0.0820 & 0.0797 & 9679.7 & 8 & 5 \\
	33	+24 & Host Type & Superhosts & 1645 & 0.0769 & 0.0729 & 5225.6 & 8 & 5 \\
	34	+25 & Host Type & Non-superhosts & 1216 & 0.0841 & 0.0787 & 4356.0 & 8 & 4 \\
	35	+\bottomrule
	36	+\end{tabular}
	37	+\par\smallskip\footnotesize
	38	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	39	+\end{table}
		\ No newline at end of file

added results/tables/table_01_summary_statistics.csv +92 −0

		`@@ -0,0 +1,92 @@`
	1	+Variable,Mean,Std. Dev.,Min,P25,Median,P75,Max,N
	2	+price_numeric,1115.17,2253.179,10.539,125.0,219.22,1139.25,24928.37,3412
	3	+rating,4.8,0.231,2.25,4.73,4.86,4.96,5.0,4190
	4	+bedrooms,2.084,1.53,0.0,1.0,2.0,3.0,25.0,5000
	5	+bathrooms,1.278,1.048,0.0,1.0,1.0,2.0,24.0,5000
	6	+guests_count,5.31,3.368,1.0,2.0,4.0,7.0,16.0,5000
	7	+amenities_count,44.918,15.985,3.0,34.0,45.0,56.0,109.0,5000
	8	+num_images,27.878,19.211,0.0,14.0,23.0,36.0,199.0,5000
	9	+num_reviews,62.748,85.02,0.0,10.0,33.0,82.0,986.0,4275
	10	+quality_score,93.78,41.945,0.0,94.5,107.9,121.0,139.8,5000
	11	+is_superhost,0.523,0.5,0.0,0.0,1.0,1.0,1.0,5000
	12	+is_guest_favorite,0.334,0.472,0.0,0.0,0.0,1.0,1.0,5000
	13	+pets_allowed,0.292,0.455,0.0,0.0,0.0,1.0,1.0,5000
	14	+price_raw,1115.17,2253.179,10.539,125.0,219.22,1139.25,24928.37,3412
	15	+price_winsor_1_99,1063.457,1903.47,38.0,125.0,219.22,1139.25,10821.939,3412
	16	+price_winsor_2_98,1025.089,1727.746,46.073,125.0,219.22,1139.25,8117.023,3412
	17	+price_winsor_5_95,920.817,1368.495,63.099,125.0,219.22,1139.25,4921.12,3412
	18	+log_price_raw,5.866,1.422,2.355,4.828,5.39,7.038,10.124,3412
	19	+log_price_winsor,5.866,1.406,3.638,4.828,5.39,7.038,9.289,3412
	20	+log_price,5.871,1.417,2.446,4.836,5.395,7.039,10.124,3412
	21	+log_accommodates,1.71,0.512,0.693,1.099,1.609,2.079,2.833,5000
	22	+log_bedrooms,1.021,0.458,0.0,0.693,1.099,1.386,3.258,5000
	23	+log_beds,1.021,0.458,0.0,0.693,1.099,1.386,3.258,5000
	24	+log_bathrooms,0.747,0.387,0.0,0.693,0.693,1.099,3.219,5000
	25	+bathrooms_per_guest,0.271,0.219,0.0,0.167,0.25,0.333,3.5,5000
	26	+bedrooms_per_guest,0.42,0.268,0.0,0.333,0.429,0.5,8.5,5000
	27	+is_entire_home,0.481,0.5,0.0,0.0,0.0,1.0,1.0,5000
	28	+is_private_room,0.355,0.479,0.0,0.0,0.0,1.0,1.0,5000
	29	+is_shared_room,0.164,0.37,0.0,0.0,0.0,0.0,1.0,5000
	30	+large_capacity_listing,0.233,0.423,0.0,0.0,0.0,0.0,1.0,5000
	31	+accommodates_sq,39.53,51.554,1.0,4.0,16.0,49.0,256.0,5000
	32	+bedrooms_sq,6.68,15.863,0.0,1.0,4.0,9.0,625.0,5000
	33	+bathrooms_sq,2.73,12.43,0.0,1.0,1.0,4.0,576.0,5000
	34	+accommodates_bedrooms,15.078,21.584,0.0,2.0,8.0,18.0,400.0,5000
	35	+accommodates_bathrooms,8.963,15.209,0.0,2.0,4.0,12.0,384.0,5000
	36	+superhost_flag,0.523,0.5,0.0,0.0,1.0,1.0,1.0,5000
	37	+host_professional_dummy,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5000
	38	+multi_listing_host,0.208,0.406,0.0,0.0,0.0,0.0,1.0,5000
	39	+superhost_x_entire_home,0.271,0.444,0.0,0.0,0.0,1.0,1.0,5000
	40	+guest_favorite_flag,0.334,0.472,0.0,0.0,0.0,1.0,1.0,5000
	41	+log_reviews,2.87,1.759,0.0,1.609,3.219,4.263,6.895,5000
	42	+review_quality,2.944,1.459,0.0,3.0,4.0,4.0,4.0,5000
	43	+reputation_score,0.649,0.29,0.0,0.641,0.761,0.827,0.995,5000
	44	+cleanliness_premium,0.426,0.495,0.0,0.0,0.0,1.0,1.0,5000
	45	+location_rating_premium,0.483,0.5,0.0,0.0,0.0,1.0,1.0,5000
	46	+has_reviews,0.836,0.37,0.0,1.0,1.0,1.0,1.0,5000
	47	+no_rating_flag,0.162,0.368,0.0,0.0,0.0,0.0,1.0,5000
	48	+price_per_guest,303.284,625.689,2.071,27.0,42.663,400.101,11812.5,3412
	49	+price_per_bedroom,607.307,1193.493,3.567,67.195,106.75,737.782,17408.68,3227
	50	+amenity_richness_index,0.395,0.151,0.0,0.292,0.396,0.5,1.0,5000
	51	+luxury_index,0.395,0.159,0.0,0.286,0.396,0.505,0.986,5000
	52	+business_travel_index,0.395,0.16,0.0,0.283,0.397,0.506,0.99,5000
	53	+family_comfort_index,0.41,0.164,0.0,0.293,0.407,0.526,1.0,5000
	54	+leisure_index,0.427,0.173,0.0,0.301,0.423,0.545,1.0,5000
	55	+remote_work_index,0.397,0.159,0.0,0.284,0.4,0.511,1.0,5000
	56	+distance_to_center,3891.274,11033.884,0.0,794.8,2089.678,4984.933,508162.01,4994
	57	+log_distance_to_center,7.134,2.225,0.0,6.679,7.645,8.514,13.139,4994
	58	+local_density_500m,14.727,25.368,0.0,0.0,2.0,14.0,125.0,4994
	59	+local_density_1km,41.977,72.505,0.0,1.0,5.0,32.0,309.0,4994
	60	+local_avg_price_500m,1107.621,1440.119,16.705,244.26,819.465,1504.689,23625.0,3309
	61	+local_avg_price_1km,1127.803,1288.588,16.705,276.11,993.98,1490.434,23625.0,3772
	62	+spatial_cluster,11.119,6.865,-1.0,5.0,13.0,18.0,19.0,5000
	63	+title_length,62.054,7.592,36.0,57.0,61.0,66.0,100.0,5000
	64	+title_word_count,14.255,1.082,8.0,14.0,14.0,15.0,20.0,5000
	65	+exclamation_count,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5000
	66	+uppercase_ratio,0.072,0.02,0.021,0.057,0.067,0.086,0.375,5000
	67	+has_star_rating,0.874,0.332,0.0,1.0,1.0,1.0,1.0,5000
	68	+extracted_star_rating,4.8,0.231,2.25,4.73,4.86,4.96,5.0,4190
	69	+luxury_language_score,0.0,0.02,0.0,0.0,0.0,0.0,1.0,5000
	70	+cozy_language_score,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5000
	71	+professional_tone_score,0.037,0.19,0.0,0.0,0.0,0.0,1.0,5000
	72	+family_language_score,0.002,0.042,0.0,0.0,0.0,0.0,1.0,5000
	73	+business_language_score,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5000
	74	+romantic_language_score,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5000
	75	+tourism_language_score,0.05,0.221,0.0,0.0,0.0,0.0,2.0,5000
	76	+parsed_bedrooms,2.117,1.519,0.0,1.0,2.0,3.0,25.0,4922
	77	+is_studio,0.05,0.217,0.0,0.0,0.0,0.0,1.0,5000
	78	+parsed_beds,3.129,2.642,1.0,1.0,2.0,4.0,58.0,4853
	79	+parsed_baths,1.439,0.976,0.0,1.0,1.0,2.0,24.0,4931
	80	+is_new_listing,0.036,0.186,0.0,0.0,0.0,0.0,1.0,5000
	81	+has_shared_bath,0.072,0.258,0.0,0.0,0.0,0.0,1.0,5000
	82	+has_private_bath,0.035,0.185,0.0,0.0,0.0,0.0,1.0,5000
	83	+text_embed_0,0.234,0.183,0.003,0.101,0.143,0.327,0.791,5000
	84	+text_embed_1,0.067,0.169,-0.447,-0.045,0.095,0.192,0.479,5000
	85	+text_embed_2,0.05,0.135,-0.451,-0.003,0.069,0.124,0.496,5000
	86	+text_embed_3,-0.006,0.122,-0.281,-0.068,-0.026,0.011,0.721,5000
	87	+text_embed_4,0.022,0.118,-0.508,-0.029,0.012,0.077,0.577,5000
	88	+text_embed_5,0.001,0.111,-0.331,-0.082,-0.004,0.065,0.531,5000
	89	+text_embed_6,-0.007,0.106,-0.449,-0.076,-0.012,0.059,0.391,5000
	90	+text_embed_7,-0.002,0.099,-0.297,-0.055,-0.006,0.035,0.486,5000
	91	+text_embed_8,0.007,0.093,-0.335,-0.041,0.01,0.043,0.459,5000
	92	+text_embed_9,0.002,0.09,-0.393,-0.037,-0.004,0.03,0.478,5000

added results/tables/table_01_summary_statistics.html +1029 −0

		`@@ -0,0 +1,1029 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>Mean</th>
	6	+ <th>Std. Dev.</th>
	7	+ <th>Min</th>
	8	+ <th>P25</th>
	9	+ <th>Median</th>
	10	+ <th>P75</th>
	11	+ <th>Max</th>
	12	+ <th>N</th>
	13	+ </tr>
	14	+ <tr>
	15	+ <th>Variable</th>
	16	+ <th></th>
	17	+ <th></th>
	18	+ <th></th>
	19	+ <th></th>
	20	+ <th></th>
	21	+ <th></th>
	22	+ <th></th>
	23	+ <th></th>
	24	+ </tr>
	25	+ </thead>
	26	+ <tbody>
	27	+ <tr>
	28	+ <th>price_numeric</th>
	29	+ <td>1115.170</td>
	30	+ <td>2253.179</td>
	31	+ <td>10.539</td>
	32	+ <td>125.000</td>
	33	+ <td>219.220</td>
	34	+ <td>1139.250</td>
	35	+ <td>24928.370</td>
	36	+ <td>3412</td>
	37	+ </tr>
	38	+ <tr>
	39	+ <th>rating</th>
	40	+ <td>4.800</td>
	41	+ <td>0.231</td>
	42	+ <td>2.250</td>
	43	+ <td>4.730</td>
	44	+ <td>4.860</td>
	45	+ <td>4.960</td>
	46	+ <td>5.000</td>
	47	+ <td>4190</td>
	48	+ </tr>
	49	+ <tr>
	50	+ <th>bedrooms</th>
	51	+ <td>2.084</td>
	52	+ <td>1.530</td>
	53	+ <td>0.000</td>
	54	+ <td>1.000</td>
	55	+ <td>2.000</td>
	56	+ <td>3.000</td>
	57	+ <td>25.000</td>
	58	+ <td>5000</td>
	59	+ </tr>
	60	+ <tr>
	61	+ <th>bathrooms</th>
	62	+ <td>1.278</td>
	63	+ <td>1.048</td>
	64	+ <td>0.000</td>
	65	+ <td>1.000</td>
	66	+ <td>1.000</td>
	67	+ <td>2.000</td>
	68	+ <td>24.000</td>
	69	+ <td>5000</td>
	70	+ </tr>
	71	+ <tr>
	72	+ <th>guests_count</th>
	73	+ <td>5.310</td>
	74	+ <td>3.368</td>
	75	+ <td>1.000</td>
	76	+ <td>2.000</td>
	77	+ <td>4.000</td>
	78	+ <td>7.000</td>
	79	+ <td>16.000</td>
	80	+ <td>5000</td>
	81	+ </tr>
	82	+ <tr>
	83	+ <th>amenities_count</th>
	84	+ <td>44.918</td>
	85	+ <td>15.985</td>
	86	+ <td>3.000</td>
	87	+ <td>34.000</td>
	88	+ <td>45.000</td>
	89	+ <td>56.000</td>
	90	+ <td>109.000</td>
	91	+ <td>5000</td>
	92	+ </tr>
	93	+ <tr>
	94	+ <th>num_images</th>
	95	+ <td>27.878</td>
	96	+ <td>19.211</td>
	97	+ <td>0.000</td>
	98	+ <td>14.000</td>
	99	+ <td>23.000</td>
	100	+ <td>36.000</td>
	101	+ <td>199.000</td>
	102	+ <td>5000</td>
	103	+ </tr>
	104	+ <tr>
	105	+ <th>num_reviews</th>
	106	+ <td>62.748</td>
	107	+ <td>85.020</td>
	108	+ <td>0.000</td>
	109	+ <td>10.000</td>
	110	+ <td>33.000</td>
	111	+ <td>82.000</td>
	112	+ <td>986.000</td>
	113	+ <td>4275</td>
	114	+ </tr>
	115	+ <tr>
	116	+ <th>quality_score</th>
	117	+ <td>93.780</td>
	118	+ <td>41.945</td>
	119	+ <td>0.000</td>
	120	+ <td>94.500</td>
	121	+ <td>107.900</td>
	122	+ <td>121.000</td>
	123	+ <td>139.800</td>
	124	+ <td>5000</td>
	125	+ </tr>
	126	+ <tr>
	127	+ <th>is_superhost</th>
	128	+ <td>0.523</td>
	129	+ <td>0.500</td>
	130	+ <td>0.000</td>
	131	+ <td>0.000</td>
	132	+ <td>1.000</td>
	133	+ <td>1.000</td>
	134	+ <td>1.000</td>
	135	+ <td>5000</td>
	136	+ </tr>
	137	+ <tr>
	138	+ <th>is_guest_favorite</th>
	139	+ <td>0.334</td>
	140	+ <td>0.472</td>
	141	+ <td>0.000</td>
	142	+ <td>0.000</td>
	143	+ <td>0.000</td>
	144	+ <td>1.000</td>
	145	+ <td>1.000</td>
	146	+ <td>5000</td>
	147	+ </tr>
	148	+ <tr>
	149	+ <th>pets_allowed</th>
	150	+ <td>0.292</td>
	151	+ <td>0.455</td>
	152	+ <td>0.000</td>
	153	+ <td>0.000</td>
	154	+ <td>0.000</td>
	155	+ <td>1.000</td>
	156	+ <td>1.000</td>
	157	+ <td>5000</td>
	158	+ </tr>
	159	+ <tr>
	160	+ <th>price_raw</th>
	161	+ <td>1115.170</td>
	162	+ <td>2253.179</td>
	163	+ <td>10.539</td>
	164	+ <td>125.000</td>
	165	+ <td>219.220</td>
	166	+ <td>1139.250</td>
	167	+ <td>24928.370</td>
	168	+ <td>3412</td>
	169	+ </tr>
	170	+ <tr>
	171	+ <th>price_winsor_1_99</th>
	172	+ <td>1063.457</td>
	173	+ <td>1903.470</td>
	174	+ <td>38.000</td>
	175	+ <td>125.000</td>
	176	+ <td>219.220</td>
	177	+ <td>1139.250</td>
	178	+ <td>10821.939</td>
	179	+ <td>3412</td>
	180	+ </tr>
	181	+ <tr>
	182	+ <th>price_winsor_2_98</th>
	183	+ <td>1025.089</td>
	184	+ <td>1727.746</td>
	185	+ <td>46.073</td>
	186	+ <td>125.000</td>
	187	+ <td>219.220</td>
	188	+ <td>1139.250</td>
	189	+ <td>8117.023</td>
	190	+ <td>3412</td>
	191	+ </tr>
	192	+ <tr>
	193	+ <th>price_winsor_5_95</th>
	194	+ <td>920.817</td>
	195	+ <td>1368.495</td>
	196	+ <td>63.099</td>
	197	+ <td>125.000</td>
	198	+ <td>219.220</td>
	199	+ <td>1139.250</td>
	200	+ <td>4921.120</td>
	201	+ <td>3412</td>
	202	+ </tr>
	203	+ <tr>
	204	+ <th>log_price_raw</th>
	205	+ <td>5.866</td>
	206	+ <td>1.422</td>
	207	+ <td>2.355</td>
	208	+ <td>4.828</td>
	209	+ <td>5.390</td>
	210	+ <td>7.038</td>
	211	+ <td>10.124</td>
	212	+ <td>3412</td>
	213	+ </tr>
	214	+ <tr>
	215	+ <th>log_price_winsor</th>
	216	+ <td>5.866</td>
	217	+ <td>1.406</td>
	218	+ <td>3.638</td>
	219	+ <td>4.828</td>
	220	+ <td>5.390</td>
	221	+ <td>7.038</td>
	222	+ <td>9.289</td>
	223	+ <td>3412</td>
	224	+ </tr>
	225	+ <tr>
	226	+ <th>log_price</th>
	227	+ <td>5.871</td>
	228	+ <td>1.417</td>
	229	+ <td>2.446</td>
	230	+ <td>4.836</td>
	231	+ <td>5.395</td>
	232	+ <td>7.039</td>
	233	+ <td>10.124</td>
	234	+ <td>3412</td>
	235	+ </tr>
	236	+ <tr>
	237	+ <th>log_accommodates</th>
	238	+ <td>1.710</td>
	239	+ <td>0.512</td>
	240	+ <td>0.693</td>
	241	+ <td>1.099</td>
	242	+ <td>1.609</td>
	243	+ <td>2.079</td>
	244	+ <td>2.833</td>
	245	+ <td>5000</td>
	246	+ </tr>
	247	+ <tr>
	248	+ <th>log_bedrooms</th>
	249	+ <td>1.021</td>
	250	+ <td>0.458</td>
	251	+ <td>0.000</td>
	252	+ <td>0.693</td>
	253	+ <td>1.099</td>
	254	+ <td>1.386</td>
	255	+ <td>3.258</td>
	256	+ <td>5000</td>
	257	+ </tr>
	258	+ <tr>
	259	+ <th>log_beds</th>
	260	+ <td>1.021</td>
	261	+ <td>0.458</td>
	262	+ <td>0.000</td>
	263	+ <td>0.693</td>
	264	+ <td>1.099</td>
	265	+ <td>1.386</td>
	266	+ <td>3.258</td>
	267	+ <td>5000</td>
	268	+ </tr>
	269	+ <tr>
	270	+ <th>log_bathrooms</th>
	271	+ <td>0.747</td>
	272	+ <td>0.387</td>
	273	+ <td>0.000</td>
	274	+ <td>0.693</td>
	275	+ <td>0.693</td>
	276	+ <td>1.099</td>
	277	+ <td>3.219</td>
	278	+ <td>5000</td>
	279	+ </tr>
	280	+ <tr>
	281	+ <th>bathrooms_per_guest</th>
	282	+ <td>0.271</td>
	283	+ <td>0.219</td>
	284	+ <td>0.000</td>
	285	+ <td>0.167</td>
	286	+ <td>0.250</td>
	287	+ <td>0.333</td>
	288	+ <td>3.500</td>
	289	+ <td>5000</td>
	290	+ </tr>
	291	+ <tr>
	292	+ <th>bedrooms_per_guest</th>
	293	+ <td>0.420</td>
	294	+ <td>0.268</td>
	295	+ <td>0.000</td>
	296	+ <td>0.333</td>
	297	+ <td>0.429</td>
	298	+ <td>0.500</td>
	299	+ <td>8.500</td>
	300	+ <td>5000</td>
	301	+ </tr>
	302	+ <tr>
	303	+ <th>is_entire_home</th>
	304	+ <td>0.481</td>
	305	+ <td>0.500</td>
	306	+ <td>0.000</td>
	307	+ <td>0.000</td>
	308	+ <td>0.000</td>
	309	+ <td>1.000</td>
	310	+ <td>1.000</td>
	311	+ <td>5000</td>
	312	+ </tr>
	313	+ <tr>
	314	+ <th>is_private_room</th>
	315	+ <td>0.355</td>
	316	+ <td>0.479</td>
	317	+ <td>0.000</td>
	318	+ <td>0.000</td>
	319	+ <td>0.000</td>
	320	+ <td>1.000</td>
	321	+ <td>1.000</td>
	322	+ <td>5000</td>
	323	+ </tr>
	324	+ <tr>
	325	+ <th>is_shared_room</th>
	326	+ <td>0.164</td>
	327	+ <td>0.370</td>
	328	+ <td>0.000</td>
	329	+ <td>0.000</td>
	330	+ <td>0.000</td>
	331	+ <td>0.000</td>
	332	+ <td>1.000</td>
	333	+ <td>5000</td>
	334	+ </tr>
	335	+ <tr>
	336	+ <th>large_capacity_listing</th>
	337	+ <td>0.233</td>
	338	+ <td>0.423</td>
	339	+ <td>0.000</td>
	340	+ <td>0.000</td>
	341	+ <td>0.000</td>
	342	+ <td>0.000</td>
	343	+ <td>1.000</td>
	344	+ <td>5000</td>
	345	+ </tr>
	346	+ <tr>
	347	+ <th>accommodates_sq</th>
	348	+ <td>39.530</td>
	349	+ <td>51.554</td>
	350	+ <td>1.000</td>
	351	+ <td>4.000</td>
	352	+ <td>16.000</td>
	353	+ <td>49.000</td>
	354	+ <td>256.000</td>
	355	+ <td>5000</td>
	356	+ </tr>
	357	+ <tr>
	358	+ <th>bedrooms_sq</th>
	359	+ <td>6.680</td>
	360	+ <td>15.863</td>
	361	+ <td>0.000</td>
	362	+ <td>1.000</td>
	363	+ <td>4.000</td>
	364	+ <td>9.000</td>
	365	+ <td>625.000</td>
	366	+ <td>5000</td>
	367	+ </tr>
	368	+ <tr>
	369	+ <th>bathrooms_sq</th>
	370	+ <td>2.730</td>
	371	+ <td>12.430</td>
	372	+ <td>0.000</td>
	373	+ <td>1.000</td>
	374	+ <td>1.000</td>
	375	+ <td>4.000</td>
	376	+ <td>576.000</td>
	377	+ <td>5000</td>
	378	+ </tr>
	379	+ <tr>
	380	+ <th>accommodates_bedrooms</th>
	381	+ <td>15.078</td>
	382	+ <td>21.584</td>
	383	+ <td>0.000</td>
	384	+ <td>2.000</td>
	385	+ <td>8.000</td>
	386	+ <td>18.000</td>
	387	+ <td>400.000</td>
	388	+ <td>5000</td>
	389	+ </tr>
	390	+ <tr>
	391	+ <th>accommodates_bathrooms</th>
	392	+ <td>8.963</td>
	393	+ <td>15.209</td>
	394	+ <td>0.000</td>
	395	+ <td>2.000</td>
	396	+ <td>4.000</td>
	397	+ <td>12.000</td>
	398	+ <td>384.000</td>
	399	+ <td>5000</td>
	400	+ </tr>
	401	+ <tr>
	402	+ <th>superhost_flag</th>
	403	+ <td>0.523</td>
	404	+ <td>0.500</td>
	405	+ <td>0.000</td>
	406	+ <td>0.000</td>
	407	+ <td>1.000</td>
	408	+ <td>1.000</td>
	409	+ <td>1.000</td>
	410	+ <td>5000</td>
	411	+ </tr>
	412	+ <tr>
	413	+ <th>host_professional_dummy</th>
	414	+ <td>0.000</td>
	415	+ <td>0.000</td>
	416	+ <td>0.000</td>
	417	+ <td>0.000</td>
	418	+ <td>0.000</td>
	419	+ <td>0.000</td>
	420	+ <td>0.000</td>
	421	+ <td>5000</td>
	422	+ </tr>
	423	+ <tr>
	424	+ <th>multi_listing_host</th>
	425	+ <td>0.208</td>
	426	+ <td>0.406</td>
	427	+ <td>0.000</td>
	428	+ <td>0.000</td>
	429	+ <td>0.000</td>
	430	+ <td>0.000</td>
	431	+ <td>1.000</td>
	432	+ <td>5000</td>
	433	+ </tr>
	434	+ <tr>
	435	+ <th>superhost_x_entire_home</th>
	436	+ <td>0.271</td>
	437	+ <td>0.444</td>
	438	+ <td>0.000</td>
	439	+ <td>0.000</td>
	440	+ <td>0.000</td>
	441	+ <td>1.000</td>
	442	+ <td>1.000</td>
	443	+ <td>5000</td>
	444	+ </tr>
	445	+ <tr>
	446	+ <th>guest_favorite_flag</th>
	447	+ <td>0.334</td>
	448	+ <td>0.472</td>
	449	+ <td>0.000</td>
	450	+ <td>0.000</td>
	451	+ <td>0.000</td>
	452	+ <td>1.000</td>
	453	+ <td>1.000</td>
	454	+ <td>5000</td>
	455	+ </tr>
	456	+ <tr>
	457	+ <th>log_reviews</th>
	458	+ <td>2.870</td>
	459	+ <td>1.759</td>
	460	+ <td>0.000</td>
	461	+ <td>1.609</td>
	462	+ <td>3.219</td>
	463	+ <td>4.263</td>
	464	+ <td>6.895</td>
	465	+ <td>5000</td>
	466	+ </tr>
	467	+ <tr>
	468	+ <th>review_quality</th>
	469	+ <td>2.944</td>
	470	+ <td>1.459</td>
	471	+ <td>0.000</td>
	472	+ <td>3.000</td>
	473	+ <td>4.000</td>
	474	+ <td>4.000</td>
	475	+ <td>4.000</td>
	476	+ <td>5000</td>
	477	+ </tr>
	478	+ <tr>
	479	+ <th>reputation_score</th>
	480	+ <td>0.649</td>
	481	+ <td>0.290</td>
	482	+ <td>0.000</td>
	483	+ <td>0.641</td>
	484	+ <td>0.761</td>
	485	+ <td>0.827</td>
	486	+ <td>0.995</td>
	487	+ <td>5000</td>
	488	+ </tr>
	489	+ <tr>
	490	+ <th>cleanliness_premium</th>
	491	+ <td>0.426</td>
	492	+ <td>0.495</td>
	493	+ <td>0.000</td>
	494	+ <td>0.000</td>
	495	+ <td>0.000</td>
	496	+ <td>1.000</td>
	497	+ <td>1.000</td>
	498	+ <td>5000</td>
	499	+ </tr>
	500	+ <tr>
	501	+ <th>location_rating_premium</th>
	502	+ <td>0.483</td>
	503	+ <td>0.500</td>
	504	+ <td>0.000</td>
	505	+ <td>0.000</td>
	506	+ <td>0.000</td>
	507	+ <td>1.000</td>
	508	+ <td>1.000</td>
	509	+ <td>5000</td>
	510	+ </tr>
	511	+ <tr>
	512	+ <th>has_reviews</th>
	513	+ <td>0.836</td>
	514	+ <td>0.370</td>
	515	+ <td>0.000</td>
	516	+ <td>1.000</td>
	517	+ <td>1.000</td>
	518	+ <td>1.000</td>
	519	+ <td>1.000</td>
	520	+ <td>5000</td>
	521	+ </tr>
	522	+ <tr>
	523	+ <th>no_rating_flag</th>
	524	+ <td>0.162</td>
	525	+ <td>0.368</td>
	526	+ <td>0.000</td>
	527	+ <td>0.000</td>
	528	+ <td>0.000</td>
	529	+ <td>0.000</td>
	530	+ <td>1.000</td>
	531	+ <td>5000</td>
	532	+ </tr>
	533	+ <tr>
	534	+ <th>price_per_guest</th>
	535	+ <td>303.284</td>
	536	+ <td>625.689</td>
	537	+ <td>2.071</td>
	538	+ <td>27.000</td>
	539	+ <td>42.663</td>
	540	+ <td>400.101</td>
	541	+ <td>11812.500</td>
	542	+ <td>3412</td>
	543	+ </tr>
	544	+ <tr>
	545	+ <th>price_per_bedroom</th>
	546	+ <td>607.307</td>
	547	+ <td>1193.493</td>
	548	+ <td>3.567</td>
	549	+ <td>67.195</td>
	550	+ <td>106.750</td>
	551	+ <td>737.782</td>
	552	+ <td>17408.680</td>
	553	+ <td>3227</td>
	554	+ </tr>
	555	+ <tr>
	556	+ <th>amenity_richness_index</th>
	557	+ <td>0.395</td>
	558	+ <td>0.151</td>
	559	+ <td>0.000</td>
	560	+ <td>0.292</td>
	561	+ <td>0.396</td>
	562	+ <td>0.500</td>
	563	+ <td>1.000</td>
	564	+ <td>5000</td>
	565	+ </tr>
	566	+ <tr>
	567	+ <th>luxury_index</th>
	568	+ <td>0.395</td>
	569	+ <td>0.159</td>
	570	+ <td>0.000</td>
	571	+ <td>0.286</td>
	572	+ <td>0.396</td>
	573	+ <td>0.505</td>
	574	+ <td>0.986</td>
	575	+ <td>5000</td>
	576	+ </tr>
	577	+ <tr>
	578	+ <th>business_travel_index</th>
	579	+ <td>0.395</td>
	580	+ <td>0.160</td>
	581	+ <td>0.000</td>
	582	+ <td>0.283</td>
	583	+ <td>0.397</td>
	584	+ <td>0.506</td>
	585	+ <td>0.990</td>
	586	+ <td>5000</td>
	587	+ </tr>
	588	+ <tr>
	589	+ <th>family_comfort_index</th>
	590	+ <td>0.410</td>
	591	+ <td>0.164</td>
	592	+ <td>0.000</td>
	593	+ <td>0.293</td>
	594	+ <td>0.407</td>
	595	+ <td>0.526</td>
	596	+ <td>1.000</td>
	597	+ <td>5000</td>
	598	+ </tr>
	599	+ <tr>
	600	+ <th>leisure_index</th>
	601	+ <td>0.427</td>
	602	+ <td>0.173</td>
	603	+ <td>0.000</td>
	604	+ <td>0.301</td>
	605	+ <td>0.423</td>
	606	+ <td>0.545</td>
	607	+ <td>1.000</td>
	608	+ <td>5000</td>
	609	+ </tr>
	610	+ <tr>
	611	+ <th>remote_work_index</th>
	612	+ <td>0.397</td>
	613	+ <td>0.159</td>
	614	+ <td>0.000</td>
	615	+ <td>0.284</td>
	616	+ <td>0.400</td>
	617	+ <td>0.511</td>
	618	+ <td>1.000</td>
	619	+ <td>5000</td>
	620	+ </tr>
	621	+ <tr>
	622	+ <th>distance_to_center</th>
	623	+ <td>3891.274</td>
	624	+ <td>11033.884</td>
	625	+ <td>0.000</td>
	626	+ <td>794.800</td>
	627	+ <td>2089.678</td>
	628	+ <td>4984.933</td>
	629	+ <td>508162.010</td>
	630	+ <td>4994</td>
	631	+ </tr>
	632	+ <tr>
	633	+ <th>log_distance_to_center</th>
	634	+ <td>7.134</td>
	635	+ <td>2.225</td>
	636	+ <td>0.000</td>
	637	+ <td>6.679</td>
	638	+ <td>7.645</td>
	639	+ <td>8.514</td>
	640	+ <td>13.139</td>
	641	+ <td>4994</td>
	642	+ </tr>
	643	+ <tr>
	644	+ <th>local_density_500m</th>
	645	+ <td>14.727</td>
	646	+ <td>25.368</td>
	647	+ <td>0.000</td>
	648	+ <td>0.000</td>
	649	+ <td>2.000</td>
	650	+ <td>14.000</td>
	651	+ <td>125.000</td>
	652	+ <td>4994</td>
	653	+ </tr>
	654	+ <tr>
	655	+ <th>local_density_1km</th>
	656	+ <td>41.977</td>
	657	+ <td>72.505</td>
	658	+ <td>0.000</td>
	659	+ <td>1.000</td>
	660	+ <td>5.000</td>
	661	+ <td>32.000</td>
	662	+ <td>309.000</td>
	663	+ <td>4994</td>
	664	+ </tr>
	665	+ <tr>
	666	+ <th>local_avg_price_500m</th>
	667	+ <td>1107.621</td>
	668	+ <td>1440.119</td>
	669	+ <td>16.705</td>
	670	+ <td>244.260</td>
	671	+ <td>819.465</td>
	672	+ <td>1504.689</td>
	673	+ <td>23625.000</td>
	674	+ <td>3309</td>
	675	+ </tr>
	676	+ <tr>
	677	+ <th>local_avg_price_1km</th>
	678	+ <td>1127.803</td>
	679	+ <td>1288.588</td>
	680	+ <td>16.705</td>
	681	+ <td>276.110</td>
	682	+ <td>993.980</td>
	683	+ <td>1490.434</td>
	684	+ <td>23625.000</td>
	685	+ <td>3772</td>
	686	+ </tr>
	687	+ <tr>
	688	+ <th>spatial_cluster</th>
	689	+ <td>11.119</td>
	690	+ <td>6.865</td>
	691	+ <td>-1.000</td>
	692	+ <td>5.000</td>
	693	+ <td>13.000</td>
	694	+ <td>18.000</td>
	695	+ <td>19.000</td>
	696	+ <td>5000</td>
	697	+ </tr>
	698	+ <tr>
	699	+ <th>title_length</th>
	700	+ <td>62.054</td>
	701	+ <td>7.592</td>
	702	+ <td>36.000</td>
	703	+ <td>57.000</td>
	704	+ <td>61.000</td>
	705	+ <td>66.000</td>
	706	+ <td>100.000</td>
	707	+ <td>5000</td>
	708	+ </tr>
	709	+ <tr>
	710	+ <th>title_word_count</th>
	711	+ <td>14.255</td>
	712	+ <td>1.082</td>
	713	+ <td>8.000</td>
	714	+ <td>14.000</td>
	715	+ <td>14.000</td>
	716	+ <td>15.000</td>
	717	+ <td>20.000</td>
	718	+ <td>5000</td>
	719	+ </tr>
	720	+ <tr>
	721	+ <th>exclamation_count</th>
	722	+ <td>0.000</td>
	723	+ <td>0.000</td>
	724	+ <td>0.000</td>
	725	+ <td>0.000</td>
	726	+ <td>0.000</td>
	727	+ <td>0.000</td>
	728	+ <td>0.000</td>
	729	+ <td>5000</td>
	730	+ </tr>
	731	+ <tr>
	732	+ <th>uppercase_ratio</th>
	733	+ <td>0.072</td>
	734	+ <td>0.020</td>
	735	+ <td>0.021</td>
	736	+ <td>0.057</td>
	737	+ <td>0.067</td>
	738	+ <td>0.086</td>
	739	+ <td>0.375</td>
	740	+ <td>5000</td>
	741	+ </tr>
	742	+ <tr>
	743	+ <th>has_star_rating</th>
	744	+ <td>0.874</td>
	745	+ <td>0.332</td>
	746	+ <td>0.000</td>
	747	+ <td>1.000</td>
	748	+ <td>1.000</td>
	749	+ <td>1.000</td>
	750	+ <td>1.000</td>
	751	+ <td>5000</td>
	752	+ </tr>
	753	+ <tr>
	754	+ <th>extracted_star_rating</th>
	755	+ <td>4.800</td>
	756	+ <td>0.231</td>
	757	+ <td>2.250</td>
	758	+ <td>4.730</td>
	759	+ <td>4.860</td>
	760	+ <td>4.960</td>
	761	+ <td>5.000</td>
	762	+ <td>4190</td>
	763	+ </tr>
	764	+ <tr>
	765	+ <th>luxury_language_score</th>
	766	+ <td>0.000</td>
	767	+ <td>0.020</td>
	768	+ <td>0.000</td>
	769	+ <td>0.000</td>
	770	+ <td>0.000</td>
	771	+ <td>0.000</td>
	772	+ <td>1.000</td>
	773	+ <td>5000</td>
	774	+ </tr>
	775	+ <tr>
	776	+ <th>cozy_language_score</th>
	777	+ <td>0.000</td>
	778	+ <td>0.000</td>
	779	+ <td>0.000</td>
	780	+ <td>0.000</td>
	781	+ <td>0.000</td>
	782	+ <td>0.000</td>
	783	+ <td>0.000</td>
	784	+ <td>5000</td>
	785	+ </tr>
	786	+ <tr>
	787	+ <th>professional_tone_score</th>
	788	+ <td>0.037</td>
	789	+ <td>0.190</td>
	790	+ <td>0.000</td>
	791	+ <td>0.000</td>
	792	+ <td>0.000</td>
	793	+ <td>0.000</td>
	794	+ <td>1.000</td>
	795	+ <td>5000</td>
	796	+ </tr>
	797	+ <tr>
	798	+ <th>family_language_score</th>
	799	+ <td>0.002</td>
	800	+ <td>0.042</td>
	801	+ <td>0.000</td>
	802	+ <td>0.000</td>
	803	+ <td>0.000</td>
	804	+ <td>0.000</td>
	805	+ <td>1.000</td>
	806	+ <td>5000</td>
	807	+ </tr>
	808	+ <tr>
	809	+ <th>business_language_score</th>
	810	+ <td>0.000</td>
	811	+ <td>0.000</td>
	812	+ <td>0.000</td>
	813	+ <td>0.000</td>
	814	+ <td>0.000</td>
	815	+ <td>0.000</td>
	816	+ <td>0.000</td>
	817	+ <td>5000</td>
	818	+ </tr>
	819	+ <tr>
	820	+ <th>romantic_language_score</th>
	821	+ <td>0.000</td>
	822	+ <td>0.000</td>
	823	+ <td>0.000</td>
	824	+ <td>0.000</td>
	825	+ <td>0.000</td>
	826	+ <td>0.000</td>
	827	+ <td>0.000</td>
	828	+ <td>5000</td>
	829	+ </tr>
	830	+ <tr>
	831	+ <th>tourism_language_score</th>
	832	+ <td>0.050</td>
	833	+ <td>0.221</td>
	834	+ <td>0.000</td>
	835	+ <td>0.000</td>
	836	+ <td>0.000</td>
	837	+ <td>0.000</td>
	838	+ <td>2.000</td>
	839	+ <td>5000</td>
	840	+ </tr>
	841	+ <tr>
	842	+ <th>parsed_bedrooms</th>
	843	+ <td>2.117</td>
	844	+ <td>1.519</td>
	845	+ <td>0.000</td>
	846	+ <td>1.000</td>
	847	+ <td>2.000</td>
	848	+ <td>3.000</td>
	849	+ <td>25.000</td>
	850	+ <td>4922</td>
	851	+ </tr>
	852	+ <tr>
	853	+ <th>is_studio</th>
	854	+ <td>0.050</td>
	855	+ <td>0.217</td>
	856	+ <td>0.000</td>
	857	+ <td>0.000</td>
	858	+ <td>0.000</td>
	859	+ <td>0.000</td>
	860	+ <td>1.000</td>
	861	+ <td>5000</td>
	862	+ </tr>
	863	+ <tr>
	864	+ <th>parsed_beds</th>
	865	+ <td>3.129</td>
	866	+ <td>2.642</td>
	867	+ <td>1.000</td>
	868	+ <td>1.000</td>
	869	+ <td>2.000</td>
	870	+ <td>4.000</td>
	871	+ <td>58.000</td>
	872	+ <td>4853</td>
	873	+ </tr>
	874	+ <tr>
	875	+ <th>parsed_baths</th>
	876	+ <td>1.439</td>
	877	+ <td>0.976</td>
	878	+ <td>0.000</td>
	879	+ <td>1.000</td>
	880	+ <td>1.000</td>
	881	+ <td>2.000</td>
	882	+ <td>24.000</td>
	883	+ <td>4931</td>
	884	+ </tr>
	885	+ <tr>
	886	+ <th>is_new_listing</th>
	887	+ <td>0.036</td>
	888	+ <td>0.186</td>
	889	+ <td>0.000</td>
	890	+ <td>0.000</td>
	891	+ <td>0.000</td>
	892	+ <td>0.000</td>
	893	+ <td>1.000</td>
	894	+ <td>5000</td>
	895	+ </tr>
	896	+ <tr>
	897	+ <th>has_shared_bath</th>
	898	+ <td>0.072</td>
	899	+ <td>0.258</td>
	900	+ <td>0.000</td>
	901	+ <td>0.000</td>
	902	+ <td>0.000</td>
	903	+ <td>0.000</td>
	904	+ <td>1.000</td>
	905	+ <td>5000</td>
	906	+ </tr>
	907	+ <tr>
	908	+ <th>has_private_bath</th>
	909	+ <td>0.035</td>
	910	+ <td>0.185</td>
	911	+ <td>0.000</td>
	912	+ <td>0.000</td>
	913	+ <td>0.000</td>
	914	+ <td>0.000</td>
	915	+ <td>1.000</td>
	916	+ <td>5000</td>
	917	+ </tr>
	918	+ <tr>
	919	+ <th>text_embed_0</th>
	920	+ <td>0.234</td>
	921	+ <td>0.183</td>
	922	+ <td>0.003</td>
	923	+ <td>0.101</td>
	924	+ <td>0.143</td>
	925	+ <td>0.327</td>
	926	+ <td>0.791</td>
	927	+ <td>5000</td>
	928	+ </tr>
	929	+ <tr>
	930	+ <th>text_embed_1</th>
	931	+ <td>0.067</td>
	932	+ <td>0.169</td>
	933	+ <td>-0.447</td>
	934	+ <td>-0.045</td>
	935	+ <td>0.095</td>
	936	+ <td>0.192</td>
	937	+ <td>0.479</td>
	938	+ <td>5000</td>
	939	+ </tr>
	940	+ <tr>
	941	+ <th>text_embed_2</th>
	942	+ <td>0.050</td>
	943	+ <td>0.135</td>
	944	+ <td>-0.451</td>
	945	+ <td>-0.003</td>
	946	+ <td>0.069</td>
	947	+ <td>0.124</td>
	948	+ <td>0.496</td>
	949	+ <td>5000</td>
	950	+ </tr>
	951	+ <tr>
	952	+ <th>text_embed_3</th>
	953	+ <td>-0.006</td>
	954	+ <td>0.122</td>
	955	+ <td>-0.281</td>
	956	+ <td>-0.068</td>
	957	+ <td>-0.026</td>
	958	+ <td>0.011</td>
	959	+ <td>0.721</td>
	960	+ <td>5000</td>
	961	+ </tr>
	962	+ <tr>
	963	+ <th>text_embed_4</th>
	964	+ <td>0.022</td>
	965	+ <td>0.118</td>
	966	+ <td>-0.508</td>
	967	+ <td>-0.029</td>
	968	+ <td>0.012</td>
	969	+ <td>0.077</td>
	970	+ <td>0.577</td>
	971	+ <td>5000</td>
	972	+ </tr>
	973	+ <tr>
	974	+ <th>text_embed_5</th>
	975	+ <td>0.001</td>
	976	+ <td>0.111</td>
	977	+ <td>-0.331</td>
	978	+ <td>-0.082</td>
	979	+ <td>-0.004</td>
	980	+ <td>0.065</td>
	981	+ <td>0.531</td>
	982	+ <td>5000</td>
	983	+ </tr>
	984	+ <tr>
	985	+ <th>text_embed_6</th>
	986	+ <td>-0.007</td>
	987	+ <td>0.106</td>
	988	+ <td>-0.449</td>
	989	+ <td>-0.076</td>
	990	+ <td>-0.012</td>
	991	+ <td>0.059</td>
	992	+ <td>0.391</td>
	993	+ <td>5000</td>
	994	+ </tr>
	995	+ <tr>
	996	+ <th>text_embed_7</th>
	997	+ <td>-0.002</td>
	998	+ <td>0.099</td>
	999	+ <td>-0.297</td>
	1000	+ <td>-0.055</td>
	1001	+ <td>-0.006</td>
	1002	+ <td>0.035</td>
	1003	+ <td>0.486</td>
	1004	+ <td>5000</td>
	1005	+ </tr>
	1006	+ <tr>
	1007	+ <th>text_embed_8</th>
	1008	+ <td>0.007</td>
	1009	+ <td>0.093</td>
	1010	+ <td>-0.335</td>
	1011	+ <td>-0.041</td>
	1012	+ <td>0.010</td>
	1013	+ <td>0.043</td>
	1014	+ <td>0.459</td>
	1015	+ <td>5000</td>
	1016	+ </tr>
	1017	+ <tr>
	1018	+ <th>text_embed_9</th>
	1019	+ <td>0.002</td>
	1020	+ <td>0.090</td>
	1021	+ <td>-0.393</td>
	1022	+ <td>-0.037</td>
	1023	+ <td>-0.004</td>
	1024	+ <td>0.030</td>
	1025	+ <td>0.478</td>
	1026	+ <td>5000</td>
	1027	+ </tr>
	1028	+ </tbody>
	1029	+</table>
		\ No newline at end of file

added results/tables/table_01_summary_statistics.tex +104 −0

		`@@ -0,0 +1,104 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Summary Statistics}
	4	+\label{tab:summary}
	5	+\begin{tabular}{lcccccccc}
	6	+\toprule
	7	+Variable & Mean & Std. Dev. & Min & P25 & Median & P75 & Max & N \\
	8	+\midrule
	9	+price_numeric & 1115.2 & 2253.2 & 10.5390 & 125.0 & 219.2 & 1139.2 & 24928.4 & 3412.0 \\
	10	+rating & 4.8000 & 0.2310 & 2.2500 & 4.7300 & 4.8600 & 4.9600 & 5.0000 & 4190.0 \\
	11	+bedrooms & 2.0840 & 1.5300 & 0.0000 & 1.0000 & 2.0000 & 3.0000 & 25.0000 & 5000.0 \\
	12	+bathrooms & 1.2780 & 1.0480 & 0.0000 & 1.0000 & 1.0000 & 2.0000 & 24.0000 & 5000.0 \\
	13	+guests_count & 5.3100 & 3.3680 & 1.0000 & 2.0000 & 4.0000 & 7.0000 & 16.0000 & 5000.0 \\
	14	+amenities_count & 44.9180 & 15.9850 & 3.0000 & 34.0000 & 45.0000 & 56.0000 & 109.0 & 5000.0 \\
	15	+num_images & 27.8780 & 19.2110 & 0.0000 & 14.0000 & 23.0000 & 36.0000 & 199.0 & 5000.0 \\
	16	+num_reviews & 62.7480 & 85.0200 & 0.0000 & 10.0000 & 33.0000 & 82.0000 & 986.0 & 4275.0 \\
	17	+quality_score & 93.7800 & 41.9450 & 0.0000 & 94.5000 & 107.9 & 121.0 & 139.8 & 5000.0 \\
	18	+is_superhost & 0.5230 & 0.5000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 1.0000 & 5000.0 \\
	19	+is_guest_favorite & 0.3340 & 0.4720 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	20	+pets_allowed & 0.2920 & 0.4550 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	21	+price_raw & 1115.2 & 2253.2 & 10.5390 & 125.0 & 219.2 & 1139.2 & 24928.4 & 3412.0 \\
	22	+price_winsor_1_99 & 1063.5 & 1903.5 & 38.0000 & 125.0 & 219.2 & 1139.2 & 10821.9 & 3412.0 \\
	23	+price_winsor_2_98 & 1025.1 & 1727.7 & 46.0730 & 125.0 & 219.2 & 1139.2 & 8117.0 & 3412.0 \\
	24	+price_winsor_5_95 & 920.8 & 1368.5 & 63.0990 & 125.0 & 219.2 & 1139.2 & 4921.1 & 3412.0 \\
	25	+log_price_raw & 5.8660 & 1.4220 & 2.3550 & 4.8280 & 5.3900 & 7.0380 & 10.1240 & 3412.0 \\
	26	+log_price_winsor & 5.8660 & 1.4060 & 3.6380 & 4.8280 & 5.3900 & 7.0380 & 9.2890 & 3412.0 \\
	27	+log_price & 5.8710 & 1.4170 & 2.4460 & 4.8360 & 5.3950 & 7.0390 & 10.1240 & 3412.0 \\
	28	+log_accommodates & 1.7100 & 0.5120 & 0.6930 & 1.0990 & 1.6090 & 2.0790 & 2.8330 & 5000.0 \\
	29	+log_bedrooms & 1.0210 & 0.4580 & 0.0000 & 0.6930 & 1.0990 & 1.3860 & 3.2580 & 5000.0 \\
	30	+log_beds & 1.0210 & 0.4580 & 0.0000 & 0.6930 & 1.0990 & 1.3860 & 3.2580 & 5000.0 \\
	31	+log_bathrooms & 0.7470 & 0.3870 & 0.0000 & 0.6930 & 0.6930 & 1.0990 & 3.2190 & 5000.0 \\
	32	+bathrooms_per_guest & 0.2710 & 0.2190 & 0.0000 & 0.1670 & 0.2500 & 0.3330 & 3.5000 & 5000.0 \\
	33	+bedrooms_per_guest & 0.4200 & 0.2680 & 0.0000 & 0.3330 & 0.4290 & 0.5000 & 8.5000 & 5000.0 \\
	34	+is_entire_home & 0.4810 & 0.5000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	35	+is_private_room & 0.3550 & 0.4790 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	36	+is_shared_room & 0.1640 & 0.3700 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	37	+large_capacity_listing & 0.2330 & 0.4230 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	38	+accommodates_sq & 39.5300 & 51.5540 & 1.0000 & 4.0000 & 16.0000 & 49.0000 & 256.0 & 5000.0 \\
	39	+bedrooms_sq & 6.6800 & 15.8630 & 0.0000 & 1.0000 & 4.0000 & 9.0000 & 625.0 & 5000.0 \\
	40	+bathrooms_sq & 2.7300 & 12.4300 & 0.0000 & 1.0000 & 1.0000 & 4.0000 & 576.0 & 5000.0 \\
	41	+accommodates_bedrooms & 15.0780 & 21.5840 & 0.0000 & 2.0000 & 8.0000 & 18.0000 & 400.0 & 5000.0 \\
	42	+accommodates_bathrooms & 8.9630 & 15.2090 & 0.0000 & 2.0000 & 4.0000 & 12.0000 & 384.0 & 5000.0 \\
	43	+superhost_flag & 0.5230 & 0.5000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 1.0000 & 5000.0 \\
	44	+host_professional_dummy & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 5000.0 \\
	45	+multi_listing_host & 0.2080 & 0.4060 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	46	+superhost_x_entire_home & 0.2710 & 0.4440 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	47	+guest_favorite_flag & 0.3340 & 0.4720 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	48	+log_reviews & 2.8700 & 1.7590 & 0.0000 & 1.6090 & 3.2190 & 4.2630 & 6.8950 & 5000.0 \\
	49	+review_quality & 2.9440 & 1.4590 & 0.0000 & 3.0000 & 4.0000 & 4.0000 & 4.0000 & 5000.0 \\
	50	+reputation_score & 0.6490 & 0.2900 & 0.0000 & 0.6410 & 0.7610 & 0.8270 & 0.9950 & 5000.0 \\
	51	+cleanliness_premium & 0.4260 & 0.4950 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	52	+location_rating_premium & 0.4830 & 0.5000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 5000.0 \\
	53	+has_reviews & 0.8360 & 0.3700 & 0.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 5000.0 \\
	54	+no_rating_flag & 0.1620 & 0.3680 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	55	+price_per_guest & 303.3 & 625.7 & 2.0710 & 27.0000 & 42.6630 & 400.1 & 11812.5 & 3412.0 \\
	56	+price_per_bedroom & 607.3 & 1193.5 & 3.5670 & 67.1950 & 106.8 & 737.8 & 17408.7 & 3227.0 \\
	57	+amenity_richness_index & 0.3950 & 0.1510 & 0.0000 & 0.2920 & 0.3960 & 0.5000 & 1.0000 & 5000.0 \\
	58	+luxury_index & 0.3950 & 0.1590 & 0.0000 & 0.2860 & 0.3960 & 0.5050 & 0.9860 & 5000.0 \\
	59	+business_travel_index & 0.3950 & 0.1600 & 0.0000 & 0.2830 & 0.3970 & 0.5060 & 0.9900 & 5000.0 \\
	60	+family_comfort_index & 0.4100 & 0.1640 & 0.0000 & 0.2930 & 0.4070 & 0.5260 & 1.0000 & 5000.0 \\
	61	+leisure_index & 0.4270 & 0.1730 & 0.0000 & 0.3010 & 0.4230 & 0.5450 & 1.0000 & 5000.0 \\
	62	+remote_work_index & 0.3970 & 0.1590 & 0.0000 & 0.2840 & 0.4000 & 0.5110 & 1.0000 & 5000.0 \\
	63	+distance_to_center & 3891.3 & 11033.9 & 0.0000 & 794.8 & 2089.7 & 4984.9 & 508162.0 & 4994.0 \\
	64	+log_distance_to_center & 7.1340 & 2.2250 & 0.0000 & 6.6790 & 7.6450 & 8.5140 & 13.1390 & 4994.0 \\
	65	+local_density_500m & 14.7270 & 25.3680 & 0.0000 & 0.0000 & 2.0000 & 14.0000 & 125.0 & 4994.0 \\
	66	+local_density_1km & 41.9770 & 72.5050 & 0.0000 & 1.0000 & 5.0000 & 32.0000 & 309.0 & 4994.0 \\
	67	+local_avg_price_500m & 1107.6 & 1440.1 & 16.7050 & 244.3 & 819.5 & 1504.7 & 23625.0 & 3309.0 \\
	68	+local_avg_price_1km & 1127.8 & 1288.6 & 16.7050 & 276.1 & 994.0 & 1490.4 & 23625.0 & 3772.0 \\
	69	+spatial_cluster & 11.1190 & 6.8650 & -1.0000 & 5.0000 & 13.0000 & 18.0000 & 19.0000 & 5000.0 \\
	70	+title_length & 62.0540 & 7.5920 & 36.0000 & 57.0000 & 61.0000 & 66.0000 & 100.0 & 5000.0 \\
	71	+title_word_count & 14.2550 & 1.0820 & 8.0000 & 14.0000 & 14.0000 & 15.0000 & 20.0000 & 5000.0 \\
	72	+exclamation_count & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 5000.0 \\
	73	+uppercase_ratio & 0.0720 & 0.0200 & 0.0210 & 0.0570 & 0.0670 & 0.0860 & 0.3750 & 5000.0 \\
	74	+has_star_rating & 0.8740 & 0.3320 & 0.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 5000.0 \\
	75	+extracted_star_rating & 4.8000 & 0.2310 & 2.2500 & 4.7300 & 4.8600 & 4.9600 & 5.0000 & 4190.0 \\
	76	+luxury_language_score & 0.0000 & 0.0200 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	77	+cozy_language_score & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 5000.0 \\
	78	+professional_tone_score & 0.0370 & 0.1900 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	79	+family_language_score & 0.0020 & 0.0420 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	80	+business_language_score & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 5000.0 \\
	81	+romantic_language_score & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 5000.0 \\
	82	+tourism_language_score & 0.0500 & 0.2210 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 2.0000 & 5000.0 \\
	83	+parsed_bedrooms & 2.1170 & 1.5190 & 0.0000 & 1.0000 & 2.0000 & 3.0000 & 25.0000 & 4922.0 \\
	84	+is_studio & 0.0500 & 0.2170 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	85	+parsed_beds & 3.1290 & 2.6420 & 1.0000 & 1.0000 & 2.0000 & 4.0000 & 58.0000 & 4853.0 \\
	86	+parsed_baths & 1.4390 & 0.9760 & 0.0000 & 1.0000 & 1.0000 & 2.0000 & 24.0000 & 4931.0 \\
	87	+is_new_listing & 0.0360 & 0.1860 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	88	+has_shared_bath & 0.0720 & 0.2580 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	89	+has_private_bath & 0.0350 & 0.1850 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5000.0 \\
	90	+text_embed_0 & 0.2340 & 0.1830 & 0.0030 & 0.1010 & 0.1430 & 0.3270 & 0.7910 & 5000.0 \\
	91	+text_embed_1 & 0.0670 & 0.1690 & -0.4470 & -0.0450 & 0.0950 & 0.1920 & 0.4790 & 5000.0 \\
	92	+text_embed_2 & 0.0500 & 0.1350 & -0.4510 & -0.0030 & 0.0690 & 0.1240 & 0.4960 & 5000.0 \\
	93	+text_embed_3 & -0.0060 & 0.1220 & -0.2810 & -0.0680 & -0.0260 & 0.0110 & 0.7210 & 5000.0 \\
	94	+text_embed_4 & 0.0220 & 0.1180 & -0.5080 & -0.0290 & 0.0120 & 0.0770 & 0.5770 & 5000.0 \\
	95	+text_embed_5 & 0.0010 & 0.1110 & -0.3310 & -0.0820 & -0.0040 & 0.0650 & 0.5310 & 5000.0 \\
	96	+text_embed_6 & -0.0070 & 0.1060 & -0.4490 & -0.0760 & -0.0120 & 0.0590 & 0.3910 & 5000.0 \\
	97	+text_embed_7 & -0.0020 & 0.0990 & -0.2970 & -0.0550 & -0.0060 & 0.0350 & 0.4860 & 5000.0 \\
	98	+text_embed_8 & 0.0070 & 0.0930 & -0.3350 & -0.0410 & 0.0100 & 0.0430 & 0.4590 & 5000.0 \\
	99	+text_embed_9 & 0.0020 & 0.0900 & -0.3930 & -0.0370 & -0.0040 & 0.0300 & 0.4780 & 5000.0 \\
	100	+\bottomrule
	101	+\end{tabular}
	102	+\par\smallskip\footnotesize
	103	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	104	+\end{table}
		\ No newline at end of file

added results/tables/table_02_price_by_type.csv +7 −0

		`@@ -0,0 +1,7 @@`
	1	+,property_type,Mean,Median,Std,Min,Max,N
	2	+0,Apartment,1276.02,200.95,3328.32,14.76,18117.0,77
	3	+1,Cabin/Chalet,969.3,236.84,2214.58,48.0,23321.0,691
	4	+2,Condo,1344.13,209.73,2342.57,49.0,14577.0,237
	5	+3,House,1058.31,222.88,2206.41,18.0,22810.62,679
	6	+4,Other,1203.11,170.0,2726.62,10.54,23625.0,554
	7	+5,Rental unit,1135.64,246.64,1925.29,14.0,24928.37,1174

added results/tables/table_02_price_by_type.html +76 −0

		`@@ -0,0 +1,76 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>property_type</th>
	6	+ <th>Mean</th>
	7	+ <th>Median</th>
	8	+ <th>Std</th>
	9	+ <th>Min</th>
	10	+ <th>Max</th>
	11	+ <th>N</th>
	12	+ </tr>
	13	+ </thead>
	14	+ <tbody>
	15	+ <tr>
	16	+ <th>0</th>
	17	+ <td>Apartment</td>
	18	+ <td>1276.02</td>
	19	+ <td>200.95</td>
	20	+ <td>3328.32</td>
	21	+ <td>14.76</td>
	22	+ <td>18117.00</td>
	23	+ <td>77</td>
	24	+ </tr>
	25	+ <tr>
	26	+ <th>1</th>
	27	+ <td>Cabin/Chalet</td>
	28	+ <td>969.30</td>
	29	+ <td>236.84</td>
	30	+ <td>2214.58</td>
	31	+ <td>48.00</td>
	32	+ <td>23321.00</td>
	33	+ <td>691</td>
	34	+ </tr>
	35	+ <tr>
	36	+ <th>2</th>
	37	+ <td>Condo</td>
	38	+ <td>1344.13</td>
	39	+ <td>209.73</td>
	40	+ <td>2342.57</td>
	41	+ <td>49.00</td>
	42	+ <td>14577.00</td>
	43	+ <td>237</td>
	44	+ </tr>
	45	+ <tr>
	46	+ <th>3</th>
	47	+ <td>House</td>
	48	+ <td>1058.31</td>
	49	+ <td>222.88</td>
	50	+ <td>2206.41</td>
	51	+ <td>18.00</td>
	52	+ <td>22810.62</td>
	53	+ <td>679</td>
	54	+ </tr>
	55	+ <tr>
	56	+ <th>4</th>
	57	+ <td>Other</td>
	58	+ <td>1203.11</td>
	59	+ <td>170.00</td>
	60	+ <td>2726.62</td>
	61	+ <td>10.54</td>
	62	+ <td>23625.00</td>
	63	+ <td>554</td>
	64	+ </tr>
	65	+ <tr>
	66	+ <th>5</th>
	67	+ <td>Rental unit</td>
	68	+ <td>1135.64</td>
	69	+ <td>246.64</td>
	70	+ <td>1925.29</td>
	71	+ <td>14.00</td>
	72	+ <td>24928.37</td>
	73	+ <td>1174</td>
	74	+ </tr>
	75	+ </tbody>
	76	+</table>
		\ No newline at end of file

added results/tables/table_02_price_by_type.tex +19 −0

		`@@ -0,0 +1,19 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Price Distribution by Property Type}
	4	+\label{tab:pricetype}
	5	+\begin{tabular}{lcccccc}
	6	+\toprule
	7	+ & property_type & Mean & Median & Std & Min & Max & N \\
	8	+\midrule
	9	+0 & Apartment & 1276.0 & 200.9 & 3328.3 & 14.7600 & 18117.0 & 77 \\
	10	+1 & Cabin/Chalet & 969.3 & 236.8 & 2214.6 & 48.0000 & 23321.0 & 691 \\
	11	+2 & Condo & 1344.1 & 209.7 & 2342.6 & 49.0000 & 14577.0 & 237 \\
	12	+3 & House & 1058.3 & 222.9 & 2206.4 & 18.0000 & 22810.6 & 679 \\
	13	+4 & Other & 1203.1 & 170.0 & 2726.6 & 10.5400 & 23625.0 & 554 \\
	14	+5 & Rental unit & 1135.6 & 246.6 & 1925.3 & 14.0000 & 24928.4 & 1174 \\
	15	+\bottomrule
	16	+\end{tabular}
	17	+\par\smallskip\footnotesize
	18	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	19	+\end{table}
		\ No newline at end of file

added results/tables/table_03_correlations.csv +8 −0

		`@@ -0,0 +1,8 @@`
	1	+,log_price,bedrooms,bathrooms,guests_count,amenities_count,rating,num_reviews
	2	+log_price,1.000,,,,,,
	3	+bedrooms,0.111***,1.000,,,,,
	4	+bathrooms,0.138*,0.704*,1.000,,,,
	5	+guests_count,0.072*,0.806,0.637**,1.000,,,
	6	+amenities_count,-0.051,0.322,0.290,0.370*,1.000,,
	7	+rating,0.006,0.129*,0.157,0.113,0.274*,1.000,
	8	+num_reviews,-0.236*,-0.061,-0.049,-0.029,0.095,0.072**,1.000

added results/tables/table_03_correlations.html +86 −0

		`@@ -0,0 +1,86 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>log_price</th>
	6	+ <th>bedrooms</th>
	7	+ <th>bathrooms</th>
	8	+ <th>guests_count</th>
	9	+ <th>amenities_count</th>
	10	+ <th>rating</th>
	11	+ <th>num_reviews</th>
	12	+ </tr>
	13	+ </thead>
	14	+ <tbody>
	15	+ <tr>
	16	+ <th>log_price</th>
	17	+ <td>1.000</td>
	18	+ <td></td>
	19	+ <td></td>
	20	+ <td></td>
	21	+ <td></td>
	22	+ <td></td>
	23	+ <td></td>
	24	+ </tr>
	25	+ <tr>
	26	+ <th>bedrooms</th>
	27	+ <td>0.111***</td>
	28	+ <td>1.000</td>
	29	+ <td></td>
	30	+ <td></td>
	31	+ <td></td>
	32	+ <td></td>
	33	+ <td></td>
	34	+ </tr>
	35	+ <tr>
	36	+ <th>bathrooms</th>
	37	+ <td>0.138***</td>
	38	+ <td>0.704***</td>
	39	+ <td>1.000</td>
	40	+ <td></td>
	41	+ <td></td>
	42	+ <td></td>
	43	+ <td></td>
	44	+ </tr>
	45	+ <tr>
	46	+ <th>guests_count</th>
	47	+ <td>0.072***</td>
	48	+ <td>0.806***</td>
	49	+ <td>0.637***</td>
	50	+ <td>1.000</td>
	51	+ <td></td>
	52	+ <td></td>
	53	+ <td></td>
	54	+ </tr>
	55	+ <tr>
	56	+ <th>amenities_count</th>
	57	+ <td>-0.051**</td>
	58	+ <td>0.322***</td>
	59	+ <td>0.290***</td>
	60	+ <td>0.370***</td>
	61	+ <td>1.000</td>
	62	+ <td></td>
	63	+ <td></td>
	64	+ </tr>
	65	+ <tr>
	66	+ <th>rating</th>
	67	+ <td>0.006</td>
	68	+ <td>0.129***</td>
	69	+ <td>0.157***</td>
	70	+ <td>0.113***</td>
	71	+ <td>0.274***</td>
	72	+ <td>1.000</td>
	73	+ <td></td>
	74	+ </tr>
	75	+ <tr>
	76	+ <th>num_reviews</th>
	77	+ <td>-0.236***</td>
	78	+ <td>-0.061**</td>
	79	+ <td>-0.049**</td>
	80	+ <td>-0.029</td>
	81	+ <td>0.095***</td>
	82	+ <td>0.072***</td>
	83	+ <td>1.000</td>
	84	+ </tr>
	85	+ </tbody>
	86	+</table>
		\ No newline at end of file

added results/tables/table_03_correlations.tex +20 −0

		`@@ -0,0 +1,20 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Correlation Matrix}
	4	+\label{tab:corr}
	5	+\begin{tabular}{lcccccc}
	6	+\toprule
	7	+ & log_price & bedrooms & bathrooms & guests_count & amenities_count & rating & num_reviews \\
	8	+\midrule
	9	+log_price & 1.000 & & & & & & \\
	10	+bedrooms & 0.111*** & 1.000 & & & & & \\
	11	+bathrooms & 0.138* & 0.704* & 1.000 & & & & \\
	12	+guests_count & 0.072* & 0.806* & 0.637*** & 1.000 & & & \\
	13	+amenities_count & -0.051 & 0.322* & 0.290* & 0.370* & 1.000 & & \\
	14	+rating & 0.006 & 0.129* & 0.157* & 0.113* & 0.274* & 1.000 & \\
	15	+num_reviews & -0.236* & -0.061 & -0.049 & -0.029 & 0.095* & 0.072*** & 1.000 \\
	16	+\bottomrule
	17	+\end{tabular}
	18	+\par\smallskip\footnotesize
	19	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	20	+\end{table}
		\ No newline at end of file

added results/tables/table_04_hedonic_regressions.csv +120 −0

		`@@ -0,0 +1,120 @@`
	1	+Variable,Model 1 - Structural,Model 2 - + Amenities,Model 3 - + Host,Model 4 - + Reviews,Model 5 - + Location,Model 6 - + Neighbourhood,Model 7 - + NLP,Model 8 - + Spatial,Model 9 - Full Hedonic
	2	+const,6.502*,6.696,6.766**,,,,,,
	3	+,(0.579),(0.591),(0.561),,,,,,
	4	+bedrooms,0.918,0.904,0.904*,0.795,0.789,0.789,0.771,1.141,1.141**
	5	+,(0.281),(0.288),(0.265),(0.257),(0.264),(0.264),(0.265),(0.311),(0.311)
	6	+bathrooms,-0.016,-0.032,-0.020,-0.105,-0.112,-0.115,-0.003,-0.164,-0.164
	7	+,(0.211),(0.216),(0.201),(0.180),(0.183),(0.183),(0.193),(0.226),(0.226)
	8	+guests_count,0.096,0.115,0.219,0.012,-0.023,-0.024,0.034,0.187,0.187
	9	+,(0.203),(0.204),(0.198),(0.195),(0.197),(0.197),(0.205),(0.252),(0.252)
	10	+is_entire_home,0.065,0.061,0.163,0.096,0.101,0.101,0.023,0.055,0.055
	11	+,(0.075),(0.075),(0.097),(0.101),(0.101),(0.101),(0.106),(0.135),(0.135)
	12	+is_private_room,0.361*,0.340,0.220,0.355,0.289,0.291,-0.375*,-0.263,-0.263
	13	+,(0.079),(0.079),(0.080),(0.078),(0.079),(0.080),(0.113),(0.135),(0.135)
	14	+log_accommodates,-0.997,-1.060,-1.327,-0.522,-0.312,-0.313,-0.657,-1.094,-1.094
	15	+,(0.824),(0.829),(0.797),(0.768),(0.778),(0.779),(0.847),(1.135),(1.135)
	16	+log_bedrooms,-1.429,-1.373,-1.433,-1.272,-1.283,-1.281,-1.299,-2.053,-2.053*
	17	+,(0.686),(0.691),(0.654),(0.613),(0.627),(0.628),(0.657),(0.774),(0.774)
	18	+log_bathrooms,0.497,0.613,0.622,0.737,0.702,0.706,0.523,0.645,0.645
	19	+,(0.426),(0.433),(0.414),(0.404),(0.410),(0.410),(0.426),(0.541),(0.541)
	20	+bathrooms_per_guest,-0.136,-0.201,-0.244,-0.401,-0.364,-0.366,-0.445*,-0.441,-0.441
	21	+,(0.201),(0.204),(0.195),(0.224),(0.229),(0.229),(0.225),(0.335),(0.335)
	22	+bedrooms_per_guest,0.008,0.011,-0.015,-0.096,-0.091,-0.090,-0.117,0.018,0.018
	23	+,(0.432),(0.436),(0.401),(0.346),(0.360),(0.362),(0.361),(0.529),(0.529)
	24	+large_capacity_listing,0.035,0.033,-0.028,0.040,0.032,0.033,0.050,-0.125,-0.125
	25	+,(0.115),(0.116),(0.114),(0.115),(0.114),(0.114),(0.113),(0.148),(0.148)
	26	+accommodates_sq,-0.002,-0.003,-0.007,-0.001,0.000,0.000,-0.001,-0.005,-0.005
	27	+,(0.006),(0.006),(0.006),(0.006),(0.006),(0.006),(0.006),(0.008),(0.008)
	28	+bedrooms_sq,-0.048,-0.048,-0.047*,-0.040,-0.039,-0.039,-0.036,-0.063,-0.063*
	29	+,(0.020),(0.021),(0.018),(0.017),(0.018),(0.018),(0.017),(0.020),(0.020)
	30	+bathrooms_sq,0.014,0.015,0.014,0.014,0.014,0.014,0.008,0.028,0.028
	31	+,(0.015),(0.016),(0.014),(0.012),(0.013),(0.013),(0.013),(0.013),(0.013)
	32	+amenities_count,,0.005,0.009,0.003,0.003,0.003,0.006,0.003,0.003
	33	+,,(0.010),(0.010),(0.010),(0.010),(0.010),(0.010),(0.013),(0.013)
	34	+amenity_richness_index,,-0.189*,-0.191,-0.129,-0.340,-0.343,-0.306*,-0.269,-0.269*
	35	+,,(0.017),(0.016),(0.046),(0.083),(0.084),(0.087),(0.106),(0.106)
	36	+luxury_index,,0.437,0.342,0.507,0.473,0.478,0.380,0.445,0.445
	37	+,,(0.474),(0.468),(0.461),(0.458),(0.458),(0.452),(0.571),(0.571)
	38	+business_travel_index,,-0.761,-0.824,-0.836,-0.799,-0.804,-0.801,-1.156,-1.156
	39	+,,(0.463),(0.451),(0.465),(0.462),(0.462),(0.456),(0.560),(0.560)
	40	+family_comfort_index,,-0.257,0.014,0.368,0.313,0.308,0.300,0.921,0.921
	41	+,,(0.490),(0.480),(0.489),(0.483),(0.483),(0.478),(0.588),(0.588)
	42	+leisure_index,,-0.132,-0.035,0.236,0.265,0.264,0.203,0.027,0.027
	43	+,,(0.481),(0.470),(0.466),(0.464),(0.464),(0.461),(0.561),(0.561)
	44	+remote_work_index,,-0.263,-0.252,-0.394,-0.312,-0.308,-0.408,-0.090,-0.090
	45	+,,(0.486),(0.479),(0.485),(0.484),(0.484),(0.478),(0.568),(0.568)
	46	+pets_allowed,,0.129,0.069,0.016,0.006,0.007,0.029,0.023,0.023
	47	+,,(0.076),(0.075),(0.076),(0.075),(0.075),(0.074),(0.093),(0.093)
	48	+is_superhost,,,-0.094*,-0.147,-0.152,-0.151,-0.114,0.039,0.039
	49	+,,,(0.037),(0.100),(0.099),(0.099),(0.095),(0.105),(0.105)
	50	+superhost_flag,,,-0.094*,-0.147,-0.152,-0.151,-0.114,0.039,0.039
	51	+,,,(0.037),(0.100),(0.099),(0.099),(0.095),(0.105),(0.105)
	52	+host_professional_dummy,,,-0.000,-0.000,0.000*,0.000,0.000**,0.000,0.000
	53	+,,,(0.000),(0.000),(0.000),(0.000),(0.000),(0.000),(0.000)
	54	+multi_listing_host,,,0.346*,0.273,0.242,0.244*,-0.025,0.002,0.002
	55	+,,,(0.068),(0.070),(0.069),(0.070),(0.078),(0.078),(0.078)
	56	+superhost_x_entire_home,,,-0.177,-0.028,-0.037,-0.037,-0.017,-0.009,-0.009
	57	+,,,(0.096),(0.102),(0.101),(0.101),(0.100),(0.121),(0.121)
	58	+guest_favorite_flag,,,-0.194**,-0.204,-0.197,-0.197,-0.142,-0.012,-0.012
	59	+,,,(0.026),(0.098),(0.097),(0.097),(0.093),(0.102),(0.102)
	60	+is_guest_favorite,,,-0.194**,-0.204,-0.197,-0.197,-0.142,-0.012,-0.012
	61	+,,,(0.026),(0.098),(0.097),(0.097),(0.093),(0.102),(0.102)
	62	+rating,,,,0.591,0.616,0.618,0.605,1.052,1.052
	63	+,,,,(0.526),(0.520),(0.521),(0.515),(0.494),(0.494)
	64	+num_reviews,,,,0.002,0.002,0.002,0.002,0.003,0.003
	65	+,,,,(0.001),(0.001),(0.001),(0.001),(0.001),(0.001)
	66	+log_reviews,,,,-0.518*,-0.521,-0.521,-0.452,-0.400,-0.400*
	67	+,,,,(0.055),(0.055),(0.055),(0.056),(0.066),(0.066)
	68	+review_quality,,,,-0.169,-0.171,-0.172,-0.177,-0.165,-0.165
	69	+,,,,(0.135),(0.134),(0.134),(0.136),(0.135),(0.135)
	70	+reputation_score,,,,0.041,0.044,0.044,0.046,0.103,0.103
	71	+,,,,(0.064),(0.063),(0.064),(0.063),(0.061),(0.061)
	72	+cleanliness_premium,,,,0.267,0.268,0.268,0.283,0.207,0.207
	73	+,,,,(0.098),(0.097),(0.098),(0.097),(0.110),(0.110)
	74	+location_rating_premium,,,,-0.047,-0.045,-0.044,-0.148,-0.146,-0.146
	75	+,,,,(0.099),(0.098),(0.099),(0.098),(0.116),(0.116)
	76	+has_reviews,,,,4.556,11.999,12.118,10.831,9.511,9.511*
	77	+,,,,(1.619),(2.929),(2.961),(3.085),(3.738),(3.738)
	78	+quality_score,,,,0.010,0.010,0.010,0.004,-0.017,-0.017
	79	+,,,,(0.018),(0.018),(0.018),(0.017),(0.018),(0.018)
	80	+distance_to_center,,,,,-0.000,-0.000,-0.000,-0.000,-0.000
	81	+,,,,,(0.000),(0.000),(0.000),(0.000),(0.000)
	82	+lat,,,,,-0.153*,-0.155,-0.095,-0.090,-0.090*
	83	+,,,,,(0.035),(0.035),(0.037),(0.045),(0.045)
	84	+long,,,,,0.008,0.008,0.009,0.004,0.004
	85	+,,,,,(0.016),(0.016),(0.016),(0.019),(0.019)
	86	+spatial_cluster,,,,,,-0.001,-0.014*,-0.013,-0.013**
	87	+,,,,,,(0.004),(0.004),(0.005),(0.005)
	88	+title_length,,,,,,,0.010*,0.007,0.007
	89	+,,,,,,,(0.004),(0.006),(0.006)
	90	+title_word_count,,,,,,,-0.094*,-0.058,-0.058
	91	+,,,,,,,(0.042),(0.053),(0.053)
	92	+luxury_language_score,,,,,,,-0.239,0.218,0.218
	93	+,,,,,,,(0.351),(196.632),(196.632)
	94	+cozy_language_score,,,,,,,0.000,0.000,0.000
	95	+,,,,,,,(0.000),(0.000),(0.000)
	96	+professional_tone_score,,,,,,,-0.173,-0.623,-0.623
	97	+,,,,,,,(0.414),(19.507),(19.507)
	98	+tourism_language_score,,,,,,,-0.112,-0.209,-0.209
	99	+,,,,,,,(0.099),(0.115),(0.115)
	100	+text_embed_0,,,,,,,3.000*,2.800,2.800**
	101	+,,,,,,,(0.353),(0.430),(0.430)
	102	+text_embed_1,,,,,,,0.247,0.479,0.479
	103	+,,,,,,,(0.366),(0.442),(0.442)
	104	+text_embed_2,,,,,,,-0.400,-0.085,-0.085
	105	+,,,,,,,(0.395),(0.460),(0.460)
	106	+text_embed_3,,,,,,,-0.035,-0.055,-0.055
	107	+,,,,,,,(0.218),(0.244),(0.244)
	108	+local_density_500m,,,,,,,,-0.008,-0.008
	109	+,,,,,,,,(0.003),(0.003)
	110	+local_density_1km,,,,,,,,0.003,0.003
	111	+,,,,,,,,(0.001),(0.001)
	112	+local_avg_price_500m,,,,,,,,0.000*,0.000*
	113	+,,,,,,,,(0.000),(0.000)
	114	+local_avg_price_1km,,,,,,,,-0.000,-0.000
	115	+,,,,,,,,(0.000),(0.000)
	116	+N,3412,3412,3412,2861,2857,2857,2857,1906,1906
	117	+R2,0.0516,0.0575,0.0985,0.1871,0.1979,0.1980,0.2249,0.2735,0.2735
	118	+Adj_R2,0.0477,0.0516,0.0918,0.1779,0.1880,0.1877,0.2125,0.2544,0.2544
	119	+AIC,11911.4,11904.2,11760.5,9381.7,9336.4,9338.3,9258.6,6103.0,6103.0
	120	+BIC,12003.4,12039.2,11920.0,9578.3,9550.9,9558.8,9532.6,6380.6,6380.6

added results/tables/table_04_hedonic_regressions.html +1458 −0

		`@@ -0,0 +1,1458 @@`
	1	+<table class="dataframe table table-sm">
	2	+ <thead>
	3	+ <tr style="text-align: right;">
	4	+ <th></th>
	5	+ <th>Model 1 - Structural</th>
	6	+ <th>Model 2 - + Amenities</th>
	7	+ <th>Model 3 - + Host</th>
	8	+ <th>Model 4 - + Reviews</th>
	9	+ <th>Model 5 - + Location</th>
	10	+ <th>Model 6 - + Neighbourhood</th>
	11	+ <th>Model 7 - + NLP</th>
	12	+ <th>Model 8 - + Spatial</th>
	13	+ <th>Model 9 - Full Hedonic</th>
	14	+ </tr>
	15	+ <tr>
	16	+ <th>Variable</th>
	17	+ <th></th>
	18	+ <th></th>
	19	+ <th></th>
	20	+ <th></th>
	21	+ <th></th>
	22	+ <th></th>
	23	+ <th></th>
	24	+ <th></th>
	25	+ <th></th>
	26	+ </tr>
	27	+ </thead>
	28	+ <tbody>
	29	+ <tr>
	30	+ <th>const</th>
	31	+ <td>6.502***</td>
	32	+ <td>6.696***</td>
	33	+ <td>6.766***</td>
	34	+ <td></td>
	35	+ <td></td>
	36	+ <td></td>
	37	+ <td></td>
	38	+ <td></td>
	39	+ <td></td>
	40	+ </tr>
	41	+ <tr>
	42	+ <th></th>
	43	+ <td>(0.579)</td>
	44	+ <td>(0.591)</td>
	45	+ <td>(0.561)</td>
	46	+ <td></td>
	47	+ <td></td>
	48	+ <td></td>
	49	+ <td></td>
	50	+ <td></td>
	51	+ <td></td>
	52	+ </tr>
	53	+ <tr>
	54	+ <th>bedrooms</th>
	55	+ <td>0.918**</td>
	56	+ <td>0.904**</td>
	57	+ <td>0.904***</td>
	58	+ <td>0.795**</td>
	59	+ <td>0.789**</td>
	60	+ <td>0.789**</td>
	61	+ <td>0.771**</td>
	62	+ <td>1.141***</td>
	63	+ <td>1.141***</td>
	64	+ </tr>
	65	+ <tr>
	66	+ <th></th>
	67	+ <td>(0.281)</td>
	68	+ <td>(0.288)</td>
	69	+ <td>(0.265)</td>
	70	+ <td>(0.257)</td>
	71	+ <td>(0.264)</td>
	72	+ <td>(0.264)</td>
	73	+ <td>(0.265)</td>
	74	+ <td>(0.311)</td>
	75	+ <td>(0.311)</td>
	76	+ </tr>
	77	+ <tr>
	78	+ <th>bathrooms</th>
	79	+ <td>-0.016</td>
	80	+ <td>-0.032</td>
	81	+ <td>-0.020</td>
	82	+ <td>-0.105</td>
	83	+ <td>-0.112</td>
	84	+ <td>-0.115</td>
	85	+ <td>-0.003</td>
	86	+ <td>-0.164</td>
	87	+ <td>-0.164</td>
	88	+ </tr>
	89	+ <tr>
	90	+ <th></th>
	91	+ <td>(0.211)</td>
	92	+ <td>(0.216)</td>
	93	+ <td>(0.201)</td>
	94	+ <td>(0.180)</td>
	95	+ <td>(0.183)</td>
	96	+ <td>(0.183)</td>
	97	+ <td>(0.193)</td>
	98	+ <td>(0.226)</td>
	99	+ <td>(0.226)</td>
	100	+ </tr>
	101	+ <tr>
	102	+ <th>guests_count</th>
	103	+ <td>0.096</td>
	104	+ <td>0.115</td>
	105	+ <td>0.219</td>
	106	+ <td>0.012</td>
	107	+ <td>-0.023</td>
	108	+ <td>-0.024</td>
	109	+ <td>0.034</td>
	110	+ <td>0.187</td>
	111	+ <td>0.187</td>
	112	+ </tr>
	113	+ <tr>
	114	+ <th></th>
	115	+ <td>(0.203)</td>
	116	+ <td>(0.204)</td>
	117	+ <td>(0.198)</td>
	118	+ <td>(0.195)</td>
	119	+ <td>(0.197)</td>
	120	+ <td>(0.197)</td>
	121	+ <td>(0.205)</td>
	122	+ <td>(0.252)</td>
	123	+ <td>(0.252)</td>
	124	+ </tr>
	125	+ <tr>
	126	+ <th>is_entire_home</th>
	127	+ <td>0.065</td>
	128	+ <td>0.061</td>
	129	+ <td>0.163</td>
	130	+ <td>0.096</td>
	131	+ <td>0.101</td>
	132	+ <td>0.101</td>
	133	+ <td>0.023</td>
	134	+ <td>0.055</td>
	135	+ <td>0.055</td>
	136	+ </tr>
	137	+ <tr>
	138	+ <th></th>
	139	+ <td>(0.075)</td>
	140	+ <td>(0.075)</td>
	141	+ <td>(0.097)</td>
	142	+ <td>(0.101)</td>
	143	+ <td>(0.101)</td>
	144	+ <td>(0.101)</td>
	145	+ <td>(0.106)</td>
	146	+ <td>(0.135)</td>
	147	+ <td>(0.135)</td>
	148	+ </tr>
	149	+ <tr>
	150	+ <th>is_private_room</th>
	151	+ <td>0.361***</td>
	152	+ <td>0.340***</td>
	153	+ <td>0.220**</td>
	154	+ <td>0.355***</td>
	155	+ <td>0.289***</td>
	156	+ <td>0.291***</td>
	157	+ <td>-0.375***</td>
	158	+ <td>-0.263</td>
	159	+ <td>-0.263</td>
	160	+ </tr>
	161	+ <tr>
	162	+ <th></th>
	163	+ <td>(0.079)</td>
	164	+ <td>(0.079)</td>
	165	+ <td>(0.080)</td>
	166	+ <td>(0.078)</td>
	167	+ <td>(0.079)</td>
	168	+ <td>(0.080)</td>
	169	+ <td>(0.113)</td>
	170	+ <td>(0.135)</td>
	171	+ <td>(0.135)</td>
	172	+ </tr>
	173	+ <tr>
	174	+ <th>log_accommodates</th>
	175	+ <td>-0.997</td>
	176	+ <td>-1.060</td>
	177	+ <td>-1.327</td>
	178	+ <td>-0.522</td>
	179	+ <td>-0.312</td>
	180	+ <td>-0.313</td>
	181	+ <td>-0.657</td>
	182	+ <td>-1.094</td>
	183	+ <td>-1.094</td>
	184	+ </tr>
	185	+ <tr>
	186	+ <th></th>
	187	+ <td>(0.824)</td>
	188	+ <td>(0.829)</td>
	189	+ <td>(0.797)</td>
	190	+ <td>(0.768)</td>
	191	+ <td>(0.778)</td>
	192	+ <td>(0.779)</td>
	193	+ <td>(0.847)</td>
	194	+ <td>(1.135)</td>
	195	+ <td>(1.135)</td>
	196	+ </tr>
	197	+ <tr>
	198	+ <th>log_bedrooms</th>
	199	+ <td>-1.429*</td>
	200	+ <td>-1.373*</td>
	201	+ <td>-1.433*</td>
	202	+ <td>-1.272*</td>
	203	+ <td>-1.283*</td>
	204	+ <td>-1.281*</td>
	205	+ <td>-1.299*</td>
	206	+ <td>-2.053**</td>
	207	+ <td>-2.053**</td>
	208	+ </tr>
	209	+ <tr>
	210	+ <th></th>
	211	+ <td>(0.686)</td>
	212	+ <td>(0.691)</td>
	213	+ <td>(0.654)</td>
	214	+ <td>(0.613)</td>
	215	+ <td>(0.627)</td>
	216	+ <td>(0.628)</td>
	217	+ <td>(0.657)</td>
	218	+ <td>(0.774)</td>
	219	+ <td>(0.774)</td>
	220	+ </tr>
	221	+ <tr>
	222	+ <th>log_bathrooms</th>
	223	+ <td>0.497</td>
	224	+ <td>0.613</td>
	225	+ <td>0.622</td>
	226	+ <td>0.737</td>
	227	+ <td>0.702</td>
	228	+ <td>0.706</td>
	229	+ <td>0.523</td>
	230	+ <td>0.645</td>
	231	+ <td>0.645</td>
	232	+ </tr>
	233	+ <tr>
	234	+ <th></th>
	235	+ <td>(0.426)</td>
	236	+ <td>(0.433)</td>
	237	+ <td>(0.414)</td>
	238	+ <td>(0.404)</td>
	239	+ <td>(0.410)</td>
	240	+ <td>(0.410)</td>
	241	+ <td>(0.426)</td>
	242	+ <td>(0.541)</td>
	243	+ <td>(0.541)</td>
	244	+ </tr>
	245	+ <tr>
	246	+ <th>bathrooms_per_guest</th>
	247	+ <td>-0.136</td>
	248	+ <td>-0.201</td>
	249	+ <td>-0.244</td>
	250	+ <td>-0.401</td>
	251	+ <td>-0.364</td>
	252	+ <td>-0.366</td>
	253	+ <td>-0.445*</td>
	254	+ <td>-0.441</td>
	255	+ <td>-0.441</td>
	256	+ </tr>
	257	+ <tr>
	258	+ <th></th>
	259	+ <td>(0.201)</td>
	260	+ <td>(0.204)</td>
	261	+ <td>(0.195)</td>
	262	+ <td>(0.224)</td>
	263	+ <td>(0.229)</td>
	264	+ <td>(0.229)</td>
	265	+ <td>(0.225)</td>
	266	+ <td>(0.335)</td>
	267	+ <td>(0.335)</td>
	268	+ </tr>
	269	+ <tr>
	270	+ <th>bedrooms_per_guest</th>
	271	+ <td>0.008</td>
	272	+ <td>0.011</td>
	273	+ <td>-0.015</td>
	274	+ <td>-0.096</td>
	275	+ <td>-0.091</td>
	276	+ <td>-0.090</td>
	277	+ <td>-0.117</td>
	278	+ <td>0.018</td>
	279	+ <td>0.018</td>
	280	+ </tr>
	281	+ <tr>
	282	+ <th></th>
	283	+ <td>(0.432)</td>
	284	+ <td>(0.436)</td>
	285	+ <td>(0.401)</td>
	286	+ <td>(0.346)</td>
	287	+ <td>(0.360)</td>
	288	+ <td>(0.362)</td>
	289	+ <td>(0.361)</td>
	290	+ <td>(0.529)</td>
	291	+ <td>(0.529)</td>
	292	+ </tr>
	293	+ <tr>
	294	+ <th>large_capacity_listing</th>
	295	+ <td>0.035</td>
	296	+ <td>0.033</td>
	297	+ <td>-0.028</td>
	298	+ <td>0.040</td>
	299	+ <td>0.032</td>
	300	+ <td>0.033</td>
	301	+ <td>0.050</td>
	302	+ <td>-0.125</td>
	303	+ <td>-0.125</td>
	304	+ </tr>
	305	+ <tr>
	306	+ <th></th>
	307	+ <td>(0.115)</td>
	308	+ <td>(0.116)</td>
	309	+ <td>(0.114)</td>
	310	+ <td>(0.115)</td>
	311	+ <td>(0.114)</td>
	312	+ <td>(0.114)</td>
	313	+ <td>(0.113)</td>
	314	+ <td>(0.148)</td>
	315	+ <td>(0.148)</td>
	316	+ </tr>
	317	+ <tr>
	318	+ <th>accommodates_sq</th>
	319	+ <td>-0.002</td>
	320	+ <td>-0.003</td>
	321	+ <td>-0.007</td>
	322	+ <td>-0.001</td>
	323	+ <td>0.000</td>
	324	+ <td>0.000</td>
	325	+ <td>-0.001</td>
	326	+ <td>-0.005</td>
	327	+ <td>-0.005</td>
	328	+ </tr>
	329	+ <tr>
	330	+ <th></th>
	331	+ <td>(0.006)</td>
	332	+ <td>(0.006)</td>
	333	+ <td>(0.006)</td>
	334	+ <td>(0.006)</td>
	335	+ <td>(0.006)</td>
	336	+ <td>(0.006)</td>
	337	+ <td>(0.006)</td>
	338	+ <td>(0.008)</td>
	339	+ <td>(0.008)</td>
	340	+ </tr>
	341	+ <tr>
	342	+ <th>bedrooms_sq</th>
	343	+ <td>-0.048*</td>
	344	+ <td>-0.048*</td>
	345	+ <td>-0.047**</td>
	346	+ <td>-0.040*</td>
	347	+ <td>-0.039*</td>
	348	+ <td>-0.039*</td>
	349	+ <td>-0.036*</td>
	350	+ <td>-0.063**</td>
	351	+ <td>-0.063**</td>
	352	+ </tr>
	353	+ <tr>
	354	+ <th></th>
	355	+ <td>(0.020)</td>
	356	+ <td>(0.021)</td>
	357	+ <td>(0.018)</td>
	358	+ <td>(0.017)</td>
	359	+ <td>(0.018)</td>
	360	+ <td>(0.018)</td>
	361	+ <td>(0.017)</td>
	362	+ <td>(0.020)</td>
	363	+ <td>(0.020)</td>
	364	+ </tr>
	365	+ <tr>
	366	+ <th>bathrooms_sq</th>
	367	+ <td>0.014</td>
	368	+ <td>0.015</td>
	369	+ <td>0.014</td>
	370	+ <td>0.014</td>
	371	+ <td>0.014</td>
	372	+ <td>0.014</td>
	373	+ <td>0.008</td>
	374	+ <td>0.028*</td>
	375	+ <td>0.028*</td>
	376	+ </tr>
	377	+ <tr>
	378	+ <th></th>
	379	+ <td>(0.015)</td>
	380	+ <td>(0.016)</td>
	381	+ <td>(0.014)</td>
	382	+ <td>(0.012)</td>
	383	+ <td>(0.013)</td>
	384	+ <td>(0.013)</td>
	385	+ <td>(0.013)</td>
	386	+ <td>(0.013)</td>
	387	+ <td>(0.013)</td>
	388	+ </tr>
	389	+ <tr>
	390	+ <th>amenities_count</th>
	391	+ <td></td>
	392	+ <td>0.005</td>
	393	+ <td>0.009</td>
	394	+ <td>0.003</td>
	395	+ <td>0.003</td>
	396	+ <td>0.003</td>
	397	+ <td>0.006</td>
	398	+ <td>0.003</td>
	399	+ <td>0.003</td>
	400	+ </tr>
	401	+ <tr>
	402	+ <th></th>
	403	+ <td></td>
	404	+ <td>(0.010)</td>
	405	+ <td>(0.010)</td>
	406	+ <td>(0.010)</td>
	407	+ <td>(0.010)</td>
	408	+ <td>(0.010)</td>
	409	+ <td>(0.010)</td>
	410	+ <td>(0.013)</td>
	411	+ <td>(0.013)</td>
	412	+ </tr>
	413	+ <tr>
	414	+ <th>amenity_richness_index</th>
	415	+ <td></td>
	416	+ <td>-0.189***</td>
	417	+ <td>-0.191***</td>
	418	+ <td>-0.129**</td>
	419	+ <td>-0.340***</td>
	420	+ <td>-0.343***</td>
	421	+ <td>-0.306***</td>
	422	+ <td>-0.269*</td>
	423	+ <td>-0.269*</td>
	424	+ </tr>
	425	+ <tr>
	426	+ <th></th>
	427	+ <td></td>
	428	+ <td>(0.017)</td>
	429	+ <td>(0.016)</td>
	430	+ <td>(0.046)</td>
	431	+ <td>(0.083)</td>
	432	+ <td>(0.084)</td>
	433	+ <td>(0.087)</td>
	434	+ <td>(0.106)</td>
	435	+ <td>(0.106)</td>
	436	+ </tr>
	437	+ <tr>
	438	+ <th>luxury_index</th>
	439	+ <td></td>
	440	+ <td>0.437</td>
	441	+ <td>0.342</td>
	442	+ <td>0.507</td>
	443	+ <td>0.473</td>
	444	+ <td>0.478</td>
	445	+ <td>0.380</td>
	446	+ <td>0.445</td>
	447	+ <td>0.445</td>
	448	+ </tr>
	449	+ <tr>
	450	+ <th></th>
	451	+ <td></td>
	452	+ <td>(0.474)</td>
	453	+ <td>(0.468)</td>
	454	+ <td>(0.461)</td>
	455	+ <td>(0.458)</td>
	456	+ <td>(0.458)</td>
	457	+ <td>(0.452)</td>
	458	+ <td>(0.571)</td>
	459	+ <td>(0.571)</td>
	460	+ </tr>
	461	+ <tr>
	462	+ <th>business_travel_index</th>
	463	+ <td></td>
	464	+ <td>-0.761</td>
	465	+ <td>-0.824</td>
	466	+ <td>-0.836</td>
	467	+ <td>-0.799</td>
	468	+ <td>-0.804</td>
	469	+ <td>-0.801</td>
	470	+ <td>-1.156*</td>
	471	+ <td>-1.156*</td>
	472	+ </tr>
	473	+ <tr>
	474	+ <th></th>
	475	+ <td></td>
	476	+ <td>(0.463)</td>
	477	+ <td>(0.451)</td>
	478	+ <td>(0.465)</td>
	479	+ <td>(0.462)</td>
	480	+ <td>(0.462)</td>
	481	+ <td>(0.456)</td>
	482	+ <td>(0.560)</td>
	483	+ <td>(0.560)</td>
	484	+ </tr>
	485	+ <tr>
	486	+ <th>family_comfort_index</th>
	487	+ <td></td>
	488	+ <td>-0.257</td>
	489	+ <td>0.014</td>
	490	+ <td>0.368</td>
	491	+ <td>0.313</td>
	492	+ <td>0.308</td>
	493	+ <td>0.300</td>
	494	+ <td>0.921</td>
	495	+ <td>0.921</td>
	496	+ </tr>
	497	+ <tr>
	498	+ <th></th>
	499	+ <td></td>
	500	+ <td>(0.490)</td>
	501	+ <td>(0.480)</td>
	502	+ <td>(0.489)</td>
	503	+ <td>(0.483)</td>
	504	+ <td>(0.483)</td>
	505	+ <td>(0.478)</td>
	506	+ <td>(0.588)</td>
	507	+ <td>(0.588)</td>
	508	+ </tr>
	509	+ <tr>
	510	+ <th>leisure_index</th>
	511	+ <td></td>
	512	+ <td>-0.132</td>
	513	+ <td>-0.035</td>
	514	+ <td>0.236</td>
	515	+ <td>0.265</td>
	516	+ <td>0.264</td>
	517	+ <td>0.203</td>
	518	+ <td>0.027</td>
	519	+ <td>0.027</td>
	520	+ </tr>
	521	+ <tr>
	522	+ <th></th>
	523	+ <td></td>
	524	+ <td>(0.481)</td>
	525	+ <td>(0.470)</td>
	526	+ <td>(0.466)</td>
	527	+ <td>(0.464)</td>
	528	+ <td>(0.464)</td>
	529	+ <td>(0.461)</td>
	530	+ <td>(0.561)</td>
	531	+ <td>(0.561)</td>
	532	+ </tr>
	533	+ <tr>
	534	+ <th>remote_work_index</th>
	535	+ <td></td>
	536	+ <td>-0.263</td>
	537	+ <td>-0.252</td>
	538	+ <td>-0.394</td>
	539	+ <td>-0.312</td>
	540	+ <td>-0.308</td>
	541	+ <td>-0.408</td>
	542	+ <td>-0.090</td>
	543	+ <td>-0.090</td>
	544	+ </tr>
	545	+ <tr>
	546	+ <th></th>
	547	+ <td></td>
	548	+ <td>(0.486)</td>
	549	+ <td>(0.479)</td>
	550	+ <td>(0.485)</td>
	551	+ <td>(0.484)</td>
	552	+ <td>(0.484)</td>
	553	+ <td>(0.478)</td>
	554	+ <td>(0.568)</td>
	555	+ <td>(0.568)</td>
	556	+ </tr>
	557	+ <tr>
	558	+ <th>pets_allowed</th>
	559	+ <td></td>
	560	+ <td>0.129</td>
	561	+ <td>0.069</td>
	562	+ <td>0.016</td>
	563	+ <td>0.006</td>
	564	+ <td>0.007</td>
	565	+ <td>0.029</td>
	566	+ <td>0.023</td>
	567	+ <td>0.023</td>
	568	+ </tr>
	569	+ <tr>
	570	+ <th></th>
	571	+ <td></td>
	572	+ <td>(0.076)</td>
	573	+ <td>(0.075)</td>
	574	+ <td>(0.076)</td>
	575	+ <td>(0.075)</td>
	576	+ <td>(0.075)</td>
	577	+ <td>(0.074)</td>
	578	+ <td>(0.093)</td>
	579	+ <td>(0.093)</td>
	580	+ </tr>
	581	+ <tr>
	582	+ <th>is_superhost</th>
	583	+ <td></td>
	584	+ <td></td>
	585	+ <td>-0.094*</td>
	586	+ <td>-0.147</td>
	587	+ <td>-0.152</td>
	588	+ <td>-0.151</td>
	589	+ <td>-0.114</td>
	590	+ <td>0.039</td>
	591	+ <td>0.039</td>
	592	+ </tr>
	593	+ <tr>
	594	+ <th></th>
	595	+ <td></td>
	596	+ <td></td>
	597	+ <td>(0.037)</td>
	598	+ <td>(0.100)</td>
	599	+ <td>(0.099)</td>
	600	+ <td>(0.099)</td>
	601	+ <td>(0.095)</td>
	602	+ <td>(0.105)</td>
	603	+ <td>(0.105)</td>
	604	+ </tr>
	605	+ <tr>
	606	+ <th>superhost_flag</th>
	607	+ <td></td>
	608	+ <td></td>
	609	+ <td>-0.094*</td>
	610	+ <td>-0.147</td>
	611	+ <td>-0.152</td>
	612	+ <td>-0.151</td>
	613	+ <td>-0.114</td>
	614	+ <td>0.039</td>
	615	+ <td>0.039</td>
	616	+ </tr>
	617	+ <tr>
	618	+ <th></th>
	619	+ <td></td>
	620	+ <td></td>
	621	+ <td>(0.037)</td>
	622	+ <td>(0.100)</td>
	623	+ <td>(0.099)</td>
	624	+ <td>(0.099)</td>
	625	+ <td>(0.095)</td>
	626	+ <td>(0.105)</td>
	627	+ <td>(0.105)</td>
	628	+ </tr>
	629	+ <tr>
	630	+ <th>host_professional_dummy</th>
	631	+ <td></td>
	632	+ <td></td>
	633	+ <td>-0.000</td>
	634	+ <td>-0.000</td>
	635	+ <td>0.000***</td>
	636	+ <td>0.000***</td>
	637	+ <td>0.000***</td>
	638	+ <td>0.000</td>
	639	+ <td>0.000</td>
	640	+ </tr>
	641	+ <tr>
	642	+ <th></th>
	643	+ <td></td>
	644	+ <td></td>
	645	+ <td>(0.000)</td>
	646	+ <td>(0.000)</td>
	647	+ <td>(0.000)</td>
	648	+ <td>(0.000)</td>
	649	+ <td>(0.000)</td>
	650	+ <td>(0.000)</td>
	651	+ <td>(0.000)</td>
	652	+ </tr>
	653	+ <tr>
	654	+ <th>multi_listing_host</th>
	655	+ <td></td>
	656	+ <td></td>
	657	+ <td>0.346***</td>
	658	+ <td>0.273***</td>
	659	+ <td>0.242***</td>
	660	+ <td>0.244***</td>
	661	+ <td>-0.025</td>
	662	+ <td>0.002</td>
	663	+ <td>0.002</td>
	664	+ </tr>
	665	+ <tr>
	666	+ <th></th>
	667	+ <td></td>
	668	+ <td></td>
	669	+ <td>(0.068)</td>
	670	+ <td>(0.070)</td>
	671	+ <td>(0.069)</td>
	672	+ <td>(0.070)</td>
	673	+ <td>(0.078)</td>
	674	+ <td>(0.078)</td>
	675	+ <td>(0.078)</td>
	676	+ </tr>
	677	+ <tr>
	678	+ <th>superhost_x_entire_home</th>
	679	+ <td></td>
	680	+ <td></td>
	681	+ <td>-0.177</td>
	682	+ <td>-0.028</td>
	683	+ <td>-0.037</td>
	684	+ <td>-0.037</td>
	685	+ <td>-0.017</td>
	686	+ <td>-0.009</td>
	687	+ <td>-0.009</td>
	688	+ </tr>
	689	+ <tr>
	690	+ <th></th>
	691	+ <td></td>
	692	+ <td></td>
	693	+ <td>(0.096)</td>
	694	+ <td>(0.102)</td>
	695	+ <td>(0.101)</td>
	696	+ <td>(0.101)</td>
	697	+ <td>(0.100)</td>
	698	+ <td>(0.121)</td>
	699	+ <td>(0.121)</td>
	700	+ </tr>
	701	+ <tr>
	702	+ <th>guest_favorite_flag</th>
	703	+ <td></td>
	704	+ <td></td>
	705	+ <td>-0.194***</td>
	706	+ <td>-0.204*</td>
	707	+ <td>-0.197*</td>
	708	+ <td>-0.197*</td>
	709	+ <td>-0.142</td>
	710	+ <td>-0.012</td>
	711	+ <td>-0.012</td>
	712	+ </tr>
	713	+ <tr>
	714	+ <th></th>
	715	+ <td></td>
	716	+ <td></td>
	717	+ <td>(0.026)</td>
	718	+ <td>(0.098)</td>
	719	+ <td>(0.097)</td>
	720	+ <td>(0.097)</td>
	721	+ <td>(0.093)</td>
	722	+ <td>(0.102)</td>
	723	+ <td>(0.102)</td>
	724	+ </tr>
	725	+ <tr>
	726	+ <th>is_guest_favorite</th>
	727	+ <td></td>
	728	+ <td></td>
	729	+ <td>-0.194***</td>
	730	+ <td>-0.204*</td>
	731	+ <td>-0.197*</td>
	732	+ <td>-0.197*</td>
	733	+ <td>-0.142</td>
	734	+ <td>-0.012</td>
	735	+ <td>-0.012</td>
	736	+ </tr>
	737	+ <tr>
	738	+ <th></th>
	739	+ <td></td>
	740	+ <td></td>
	741	+ <td>(0.026)</td>
	742	+ <td>(0.098)</td>
	743	+ <td>(0.097)</td>
	744	+ <td>(0.097)</td>
	745	+ <td>(0.093)</td>
	746	+ <td>(0.102)</td>
	747	+ <td>(0.102)</td>
	748	+ </tr>
	749	+ <tr>
	750	+ <th>rating</th>
	751	+ <td></td>
	752	+ <td></td>
	753	+ <td></td>
	754	+ <td>0.591</td>
	755	+ <td>0.616</td>
	756	+ <td>0.618</td>
	757	+ <td>0.605</td>
	758	+ <td>1.052*</td>
	759	+ <td>1.052*</td>
	760	+ </tr>
	761	+ <tr>
	762	+ <th></th>
	763	+ <td></td>
	764	+ <td></td>
	765	+ <td></td>
	766	+ <td>(0.526)</td>
	767	+ <td>(0.520)</td>
	768	+ <td>(0.521)</td>
	769	+ <td>(0.515)</td>
	770	+ <td>(0.494)</td>
	771	+ <td>(0.494)</td>
	772	+ </tr>
	773	+ <tr>
	774	+ <th>num_reviews</th>
	775	+ <td></td>
	776	+ <td></td>
	777	+ <td></td>
	778	+ <td>0.002</td>
	779	+ <td>0.002</td>
	780	+ <td>0.002</td>
	781	+ <td>0.002</td>
	782	+ <td>0.003**</td>
	783	+ <td>0.003**</td>
	784	+ </tr>
	785	+ <tr>
	786	+ <th></th>
	787	+ <td></td>
	788	+ <td></td>
	789	+ <td></td>
	790	+ <td>(0.001)</td>
	791	+ <td>(0.001)</td>
	792	+ <td>(0.001)</td>
	793	+ <td>(0.001)</td>
	794	+ <td>(0.001)</td>
	795	+ <td>(0.001)</td>
	796	+ </tr>
	797	+ <tr>
	798	+ <th>log_reviews</th>
	799	+ <td></td>
	800	+ <td></td>
	801	+ <td></td>
	802	+ <td>-0.518***</td>
	803	+ <td>-0.521***</td>
	804	+ <td>-0.521***</td>
	805	+ <td>-0.452***</td>
	806	+ <td>-0.400***</td>
	807	+ <td>-0.400***</td>
	808	+ </tr>
	809	+ <tr>
	810	+ <th></th>
	811	+ <td></td>
	812	+ <td></td>
	813	+ <td></td>
	814	+ <td>(0.055)</td>
	815	+ <td>(0.055)</td>
	816	+ <td>(0.055)</td>
	817	+ <td>(0.056)</td>
	818	+ <td>(0.066)</td>
	819	+ <td>(0.066)</td>
	820	+ </tr>
	821	+ <tr>
	822	+ <th>review_quality</th>
	823	+ <td></td>
	824	+ <td></td>
	825	+ <td></td>
	826	+ <td>-0.169</td>
	827	+ <td>-0.171</td>
	828	+ <td>-0.172</td>
	829	+ <td>-0.177</td>
	830	+ <td>-0.165</td>
	831	+ <td>-0.165</td>
	832	+ </tr>
	833	+ <tr>
	834	+ <th></th>
	835	+ <td></td>
	836	+ <td></td>
	837	+ <td></td>
	838	+ <td>(0.135)</td>
	839	+ <td>(0.134)</td>
	840	+ <td>(0.134)</td>
	841	+ <td>(0.136)</td>
	842	+ <td>(0.135)</td>
	843	+ <td>(0.135)</td>
	844	+ </tr>
	845	+ <tr>
	846	+ <th>reputation_score</th>
	847	+ <td></td>
	848	+ <td></td>
	849	+ <td></td>
	850	+ <td>0.041</td>
	851	+ <td>0.044</td>
	852	+ <td>0.044</td>
	853	+ <td>0.046</td>
	854	+ <td>0.103</td>
	855	+ <td>0.103</td>
	856	+ </tr>
	857	+ <tr>
	858	+ <th></th>
	859	+ <td></td>
	860	+ <td></td>
	861	+ <td></td>
	862	+ <td>(0.064)</td>
	863	+ <td>(0.063)</td>
	864	+ <td>(0.064)</td>
	865	+ <td>(0.063)</td>
	866	+ <td>(0.061)</td>
	867	+ <td>(0.061)</td>
	868	+ </tr>
	869	+ <tr>
	870	+ <th>cleanliness_premium</th>
	871	+ <td></td>
	872	+ <td></td>
	873	+ <td></td>
	874	+ <td>0.267**</td>
	875	+ <td>0.268**</td>
	876	+ <td>0.268**</td>
	877	+ <td>0.283**</td>
	878	+ <td>0.207</td>
	879	+ <td>0.207</td>
	880	+ </tr>
	881	+ <tr>
	882	+ <th></th>
	883	+ <td></td>
	884	+ <td></td>
	885	+ <td></td>
	886	+ <td>(0.098)</td>
	887	+ <td>(0.097)</td>
	888	+ <td>(0.098)</td>
	889	+ <td>(0.097)</td>
	890	+ <td>(0.110)</td>
	891	+ <td>(0.110)</td>
	892	+ </tr>
	893	+ <tr>
	894	+ <th>location_rating_premium</th>
	895	+ <td></td>
	896	+ <td></td>
	897	+ <td></td>
	898	+ <td>-0.047</td>
	899	+ <td>-0.045</td>
	900	+ <td>-0.044</td>
	901	+ <td>-0.148</td>
	902	+ <td>-0.146</td>
	903	+ <td>-0.146</td>
	904	+ </tr>
	905	+ <tr>
	906	+ <th></th>
	907	+ <td></td>
	908	+ <td></td>
	909	+ <td></td>
	910	+ <td>(0.099)</td>
	911	+ <td>(0.098)</td>
	912	+ <td>(0.099)</td>
	913	+ <td>(0.098)</td>
	914	+ <td>(0.116)</td>
	915	+ <td>(0.116)</td>
	916	+ </tr>
	917	+ <tr>
	918	+ <th>has_reviews</th>
	919	+ <td></td>
	920	+ <td></td>
	921	+ <td></td>
	922	+ <td>4.556**</td>
	923	+ <td>11.999***</td>
	924	+ <td>12.118***</td>
	925	+ <td>10.831***</td>
	926	+ <td>9.511*</td>
	927	+ <td>9.511*</td>
	928	+ </tr>
	929	+ <tr>
	930	+ <th></th>
	931	+ <td></td>
	932	+ <td></td>
	933	+ <td></td>
	934	+ <td>(1.619)</td>
	935	+ <td>(2.929)</td>
	936	+ <td>(2.961)</td>
	937	+ <td>(3.085)</td>
	938	+ <td>(3.738)</td>
	939	+ <td>(3.738)</td>
	940	+ </tr>
	941	+ <tr>
	942	+ <th>quality_score</th>
	943	+ <td></td>
	944	+ <td></td>
	945	+ <td></td>
	946	+ <td>0.010</td>
	947	+ <td>0.010</td>
	948	+ <td>0.010</td>
	949	+ <td>0.004</td>
	950	+ <td>-0.017</td>
	951	+ <td>-0.017</td>
	952	+ </tr>
	953	+ <tr>
	954	+ <th></th>
	955	+ <td></td>
	956	+ <td></td>
	957	+ <td></td>
	958	+ <td>(0.018)</td>
	959	+ <td>(0.018)</td>
	960	+ <td>(0.018)</td>
	961	+ <td>(0.017)</td>
	962	+ <td>(0.018)</td>
	963	+ <td>(0.018)</td>
	964	+ </tr>
	965	+ <tr>
	966	+ <th>distance_to_center</th>
	967	+ <td></td>
	968	+ <td></td>
	969	+ <td></td>
	970	+ <td></td>
	971	+ <td>-0.000</td>
	972	+ <td>-0.000</td>
	973	+ <td>-0.000</td>
	974	+ <td>-0.000</td>
	975	+ <td>-0.000</td>
	976	+ </tr>
	977	+ <tr>
	978	+ <th></th>
	979	+ <td></td>
	980	+ <td></td>
	981	+ <td></td>
	982	+ <td></td>
	983	+ <td>(0.000)</td>
	984	+ <td>(0.000)</td>
	985	+ <td>(0.000)</td>
	986	+ <td>(0.000)</td>
	987	+ <td>(0.000)</td>
	988	+ </tr>
	989	+ <tr>
	990	+ <th>lat</th>
	991	+ <td></td>
	992	+ <td></td>
	993	+ <td></td>
	994	+ <td></td>
	995	+ <td>-0.153***</td>
	996	+ <td>-0.155***</td>
	997	+ <td>-0.095**</td>
	998	+ <td>-0.090*</td>
	999	+ <td>-0.090*</td>
	1000	+ </tr>
	1001	+ <tr>
	1002	+ <th></th>
	1003	+ <td></td>
	1004	+ <td></td>
	1005	+ <td></td>
	1006	+ <td></td>
	1007	+ <td>(0.035)</td>
	1008	+ <td>(0.035)</td>
	1009	+ <td>(0.037)</td>
	1010	+ <td>(0.045)</td>
	1011	+ <td>(0.045)</td>
	1012	+ </tr>
	1013	+ <tr>
	1014	+ <th>long</th>
	1015	+ <td></td>
	1016	+ <td></td>
	1017	+ <td></td>
	1018	+ <td></td>
	1019	+ <td>0.008</td>
	1020	+ <td>0.008</td>
	1021	+ <td>0.009</td>
	1022	+ <td>0.004</td>
	1023	+ <td>0.004</td>
	1024	+ </tr>
	1025	+ <tr>
	1026	+ <th></th>
	1027	+ <td></td>
	1028	+ <td></td>
	1029	+ <td></td>
	1030	+ <td></td>
	1031	+ <td>(0.016)</td>
	1032	+ <td>(0.016)</td>
	1033	+ <td>(0.016)</td>
	1034	+ <td>(0.019)</td>
	1035	+ <td>(0.019)</td>
	1036	+ </tr>
	1037	+ <tr>
	1038	+ <th>spatial_cluster</th>
	1039	+ <td></td>
	1040	+ <td></td>
	1041	+ <td></td>
	1042	+ <td></td>
	1043	+ <td></td>
	1044	+ <td>-0.001</td>
	1045	+ <td>-0.014***</td>
	1046	+ <td>-0.013**</td>
	1047	+ <td>-0.013**</td>
	1048	+ </tr>
	1049	+ <tr>
	1050	+ <th></th>
	1051	+ <td></td>
	1052	+ <td></td>
	1053	+ <td></td>
	1054	+ <td></td>
	1055	+ <td></td>
	1056	+ <td>(0.004)</td>
	1057	+ <td>(0.004)</td>
	1058	+ <td>(0.005)</td>
	1059	+ <td>(0.005)</td>
	1060	+ </tr>
	1061	+ <tr>
	1062	+ <th>title_length</th>
	1063	+ <td></td>
	1064	+ <td></td>
	1065	+ <td></td>
	1066	+ <td></td>
	1067	+ <td></td>
	1068	+ <td></td>
	1069	+ <td>0.010*</td>
	1070	+ <td>0.007</td>
	1071	+ <td>0.007</td>
	1072	+ </tr>
	1073	+ <tr>
	1074	+ <th></th>
	1075	+ <td></td>
	1076	+ <td></td>
	1077	+ <td></td>
	1078	+ <td></td>
	1079	+ <td></td>
	1080	+ <td></td>
	1081	+ <td>(0.004)</td>
	1082	+ <td>(0.006)</td>
	1083	+ <td>(0.006)</td>
	1084	+ </tr>
	1085	+ <tr>
	1086	+ <th>title_word_count</th>
	1087	+ <td></td>
	1088	+ <td></td>
	1089	+ <td></td>
	1090	+ <td></td>
	1091	+ <td></td>
	1092	+ <td></td>
	1093	+ <td>-0.094*</td>
	1094	+ <td>-0.058</td>
	1095	+ <td>-0.058</td>
	1096	+ </tr>
	1097	+ <tr>
	1098	+ <th></th>
	1099	+ <td></td>
	1100	+ <td></td>
	1101	+ <td></td>
	1102	+ <td></td>
	1103	+ <td></td>
	1104	+ <td></td>
	1105	+ <td>(0.042)</td>
	1106	+ <td>(0.053)</td>
	1107	+ <td>(0.053)</td>
	1108	+ </tr>
	1109	+ <tr>
	1110	+ <th>luxury_language_score</th>
	1111	+ <td></td>
	1112	+ <td></td>
	1113	+ <td></td>
	1114	+ <td></td>
	1115	+ <td></td>
	1116	+ <td></td>
	1117	+ <td>-0.239</td>
	1118	+ <td>0.218</td>
	1119	+ <td>0.218</td>
	1120	+ </tr>
	1121	+ <tr>
	1122	+ <th></th>
	1123	+ <td></td>
	1124	+ <td></td>
	1125	+ <td></td>
	1126	+ <td></td>
	1127	+ <td></td>
	1128	+ <td></td>
	1129	+ <td>(0.351)</td>
	1130	+ <td>(196.632)</td>
	1131	+ <td>(196.632)</td>
	1132	+ </tr>
	1133	+ <tr>
	1134	+ <th>cozy_language_score</th>
	1135	+ <td></td>
	1136	+ <td></td>
	1137	+ <td></td>
	1138	+ <td></td>
	1139	+ <td></td>
	1140	+ <td></td>
	1141	+ <td>0.000</td>
	1142	+ <td>0.000</td>
	1143	+ <td>0.000</td>
	1144	+ </tr>
	1145	+ <tr>
	1146	+ <th></th>
	1147	+ <td></td>
	1148	+ <td></td>
	1149	+ <td></td>
	1150	+ <td></td>
	1151	+ <td></td>
	1152	+ <td></td>
	1153	+ <td>(0.000)</td>
	1154	+ <td>(0.000)</td>
	1155	+ <td>(0.000)</td>
	1156	+ </tr>
	1157	+ <tr>
	1158	+ <th>professional_tone_score</th>
	1159	+ <td></td>
	1160	+ <td></td>
	1161	+ <td></td>
	1162	+ <td></td>
	1163	+ <td></td>
	1164	+ <td></td>
	1165	+ <td>-0.173</td>
	1166	+ <td>-0.623</td>
	1167	+ <td>-0.623</td>
	1168	+ </tr>
	1169	+ <tr>
	1170	+ <th></th>
	1171	+ <td></td>
	1172	+ <td></td>
	1173	+ <td></td>
	1174	+ <td></td>
	1175	+ <td></td>
	1176	+ <td></td>
	1177	+ <td>(0.414)</td>
	1178	+ <td>(19.507)</td>
	1179	+ <td>(19.507)</td>
	1180	+ </tr>
	1181	+ <tr>
	1182	+ <th>tourism_language_score</th>
	1183	+ <td></td>
	1184	+ <td></td>
	1185	+ <td></td>
	1186	+ <td></td>
	1187	+ <td></td>
	1188	+ <td></td>
	1189	+ <td>-0.112</td>
	1190	+ <td>-0.209</td>
	1191	+ <td>-0.209</td>
	1192	+ </tr>
	1193	+ <tr>
	1194	+ <th></th>
	1195	+ <td></td>
	1196	+ <td></td>
	1197	+ <td></td>
	1198	+ <td></td>
	1199	+ <td></td>
	1200	+ <td></td>
	1201	+ <td>(0.099)</td>
	1202	+ <td>(0.115)</td>
	1203	+ <td>(0.115)</td>
	1204	+ </tr>
	1205	+ <tr>
	1206	+ <th>text_embed_0</th>
	1207	+ <td></td>
	1208	+ <td></td>
	1209	+ <td></td>
	1210	+ <td></td>
	1211	+ <td></td>
	1212	+ <td></td>
	1213	+ <td>3.000***</td>
	1214	+ <td>2.800***</td>
	1215	+ <td>2.800***</td>
	1216	+ </tr>
	1217	+ <tr>
	1218	+ <th></th>
	1219	+ <td></td>
	1220	+ <td></td>
	1221	+ <td></td>
	1222	+ <td></td>
	1223	+ <td></td>
	1224	+ <td></td>
	1225	+ <td>(0.353)</td>
	1226	+ <td>(0.430)</td>
	1227	+ <td>(0.430)</td>
	1228	+ </tr>
	1229	+ <tr>
	1230	+ <th>text_embed_1</th>
	1231	+ <td></td>
	1232	+ <td></td>
	1233	+ <td></td>
	1234	+ <td></td>
	1235	+ <td></td>
	1236	+ <td></td>
	1237	+ <td>0.247</td>
	1238	+ <td>0.479</td>
	1239	+ <td>0.479</td>
	1240	+ </tr>
	1241	+ <tr>
	1242	+ <th></th>
	1243	+ <td></td>
	1244	+ <td></td>
	1245	+ <td></td>
	1246	+ <td></td>
	1247	+ <td></td>
	1248	+ <td></td>
	1249	+ <td>(0.366)</td>
	1250	+ <td>(0.442)</td>
	1251	+ <td>(0.442)</td>
	1252	+ </tr>
	1253	+ <tr>
	1254	+ <th>text_embed_2</th>
	1255	+ <td></td>
	1256	+ <td></td>
	1257	+ <td></td>
	1258	+ <td></td>
	1259	+ <td></td>
	1260	+ <td></td>
	1261	+ <td>-0.400</td>
	1262	+ <td>-0.085</td>
	1263	+ <td>-0.085</td>
	1264	+ </tr>
	1265	+ <tr>
	1266	+ <th></th>
	1267	+ <td></td>
	1268	+ <td></td>
	1269	+ <td></td>
	1270	+ <td></td>
	1271	+ <td></td>
	1272	+ <td></td>
	1273	+ <td>(0.395)</td>
	1274	+ <td>(0.460)</td>
	1275	+ <td>(0.460)</td>
	1276	+ </tr>
	1277	+ <tr>
	1278	+ <th>text_embed_3</th>
	1279	+ <td></td>
	1280	+ <td></td>
	1281	+ <td></td>
	1282	+ <td></td>
	1283	+ <td></td>
	1284	+ <td></td>
	1285	+ <td>-0.035</td>
	1286	+ <td>-0.055</td>
	1287	+ <td>-0.055</td>
	1288	+ </tr>
	1289	+ <tr>
	1290	+ <th></th>
	1291	+ <td></td>
	1292	+ <td></td>
	1293	+ <td></td>
	1294	+ <td></td>
	1295	+ <td></td>
	1296	+ <td></td>
	1297	+ <td>(0.218)</td>
	1298	+ <td>(0.244)</td>
	1299	+ <td>(0.244)</td>
	1300	+ </tr>
	1301	+ <tr>
	1302	+ <th>local_density_500m</th>
	1303	+ <td></td>
	1304	+ <td></td>
	1305	+ <td></td>
	1306	+ <td></td>
	1307	+ <td></td>
	1308	+ <td></td>
	1309	+ <td></td>
	1310	+ <td>-0.008**</td>
	1311	+ <td>-0.008**</td>
	1312	+ </tr>
	1313	+ <tr>
	1314	+ <th></th>
	1315	+ <td></td>
	1316	+ <td></td>
	1317	+ <td></td>
	1318	+ <td></td>
	1319	+ <td></td>
	1320	+ <td></td>
	1321	+ <td></td>
	1322	+ <td>(0.003)</td>
	1323	+ <td>(0.003)</td>
	1324	+ </tr>
	1325	+ <tr>
	1326	+ <th>local_density_1km</th>
	1327	+ <td></td>
	1328	+ <td></td>
	1329	+ <td></td>
	1330	+ <td></td>
	1331	+ <td></td>
	1332	+ <td></td>
	1333	+ <td></td>
	1334	+ <td>0.003**</td>
	1335	+ <td>0.003**</td>
	1336	+ </tr>
	1337	+ <tr>
	1338	+ <th></th>
	1339	+ <td></td>
	1340	+ <td></td>
	1341	+ <td></td>
	1342	+ <td></td>
	1343	+ <td></td>
	1344	+ <td></td>
	1345	+ <td></td>
	1346	+ <td>(0.001)</td>
	1347	+ <td>(0.001)</td>
	1348	+ </tr>
	1349	+ <tr>
	1350	+ <th>local_avg_price_500m</th>
	1351	+ <td></td>
	1352	+ <td></td>
	1353	+ <td></td>
	1354	+ <td></td>
	1355	+ <td></td>
	1356	+ <td></td>
	1357	+ <td></td>
	1358	+ <td>0.000***</td>
	1359	+ <td>0.000***</td>
	1360	+ </tr>
	1361	+ <tr>
	1362	+ <th></th>
	1363	+ <td></td>
	1364	+ <td></td>
	1365	+ <td></td>
	1366	+ <td></td>
	1367	+ <td></td>
	1368	+ <td></td>
	1369	+ <td></td>
	1370	+ <td>(0.000)</td>
	1371	+ <td>(0.000)</td>
	1372	+ </tr>
	1373	+ <tr>
	1374	+ <th>local_avg_price_1km</th>
	1375	+ <td></td>
	1376	+ <td></td>
	1377	+ <td></td>
	1378	+ <td></td>
	1379	+ <td></td>
	1380	+ <td></td>
	1381	+ <td></td>
	1382	+ <td>-0.000</td>
	1383	+ <td>-0.000</td>
	1384	+ </tr>
	1385	+ <tr>
	1386	+ <th></th>
	1387	+ <td></td>
	1388	+ <td></td>
	1389	+ <td></td>
	1390	+ <td></td>
	1391	+ <td></td>
	1392	+ <td></td>
	1393	+ <td></td>
	1394	+ <td>(0.000)</td>
	1395	+ <td>(0.000)</td>
	1396	+ </tr>
	1397	+ <tr>
	1398	+ <th>N</th>
	1399	+ <td>3412</td>
	1400	+ <td>3412</td>
	1401	+ <td>3412</td>
	1402	+ <td>2861</td>
	1403	+ <td>2857</td>
	1404	+ <td>2857</td>
	1405	+ <td>2857</td>
	1406	+ <td>1906</td>
	1407	+ <td>1906</td>
	1408	+ </tr>
	1409	+ <tr>
	1410	+ <th>R2</th>
	1411	+ <td>0.0516</td>
	1412	+ <td>0.0575</td>
	1413	+ <td>0.0985</td>
	1414	+ <td>0.1871</td>
	1415	+ <td>0.1979</td>
	1416	+ <td>0.1980</td>
	1417	+ <td>0.2249</td>
	1418	+ <td>0.2735</td>
	1419	+ <td>0.2735</td>
	1420	+ </tr>
	1421	+ <tr>
	1422	+ <th>Adj_R2</th>
	1423	+ <td>0.0477</td>
	1424	+ <td>0.0516</td>
	1425	+ <td>0.0918</td>
	1426	+ <td>0.1779</td>
	1427	+ <td>0.1880</td>
	1428	+ <td>0.1877</td>
	1429	+ <td>0.2125</td>
	1430	+ <td>0.2544</td>
	1431	+ <td>0.2544</td>
	1432	+ </tr>
	1433	+ <tr>
	1434	+ <th>AIC</th>
	1435	+ <td>11911.4</td>
	1436	+ <td>11904.2</td>
	1437	+ <td>11760.5</td>
	1438	+ <td>9381.7</td>
	1439	+ <td>9336.4</td>
	1440	+ <td>9338.3</td>
	1441	+ <td>9258.6</td>
	1442	+ <td>6103.0</td>
	1443	+ <td>6103.0</td>
	1444	+ </tr>
	1445	+ <tr>
	1446	+ <th>BIC</th>
	1447	+ <td>12003.4</td>
	1448	+ <td>12039.2</td>
	1449	+ <td>11920.0</td>
	1450	+ <td>9578.3</td>
	1451	+ <td>9550.9</td>
	1452	+ <td>9558.8</td>
	1453	+ <td>9532.6</td>
	1454	+ <td>6380.6</td>
	1455	+ <td>6380.6</td>
	1456	+ </tr>
	1457	+ </tbody>
	1458	+</table>
		\ No newline at end of file

added results/tables/table_04_hedonic_regressions.tex +132 −0

		`@@ -0,0 +1,132 @@`
	1	+\begin{table}[htbp]
	2	+\centering
	3	+\caption{Hedonic Regression Results}
	4	+\label{tab:hedonic}
	5	+\begin{tabular}{lccccccccc}
	6	+\toprule
	7	+Variable & Model 1 - Structural & Model 2 - + Amenities & Model 3 - + Host & Model 4 - + Reviews & Model 5 - + Location & Model 6 - + Neighbourhood & Model 7 - + NLP & Model 8 - + Spatial & Model 9 - Full Hedonic \\
	8	+\midrule
	9	+const & 6.502* & 6.696* & 6.766*** & & & & & & \\
	10	+ & (0.579) & (0.591) & (0.561) & & & & & & \\
	11	+bedrooms & 0.918 & 0.904 & 0.904* & 0.795 & 0.789 & 0.789 & 0.771 & 1.141* & 1.141*** \\
	12	+ & (0.281) & (0.288) & (0.265) & (0.257) & (0.264) & (0.264) & (0.265) & (0.311) & (0.311) \\
	13	+bathrooms & -0.016 & -0.032 & -0.020 & -0.105 & -0.112 & -0.115 & -0.003 & -0.164 & -0.164 \\
	14	+ & (0.211) & (0.216) & (0.201) & (0.180) & (0.183) & (0.183) & (0.193) & (0.226) & (0.226) \\
	15	+guests_count & 0.096 & 0.115 & 0.219 & 0.012 & -0.023 & -0.024 & 0.034 & 0.187 & 0.187 \\
	16	+ & (0.203) & (0.204) & (0.198) & (0.195) & (0.197) & (0.197) & (0.205) & (0.252) & (0.252) \\
	17	+is_entire_home & 0.065 & 0.061 & 0.163 & 0.096 & 0.101 & 0.101 & 0.023 & 0.055 & 0.055 \\
	18	+ & (0.075) & (0.075) & (0.097) & (0.101) & (0.101) & (0.101) & (0.106) & (0.135) & (0.135) \\
	19	+is_private_room & 0.361* & 0.340* & 0.220 & 0.355* & 0.289* & 0.291* & -0.375*** & -0.263 & -0.263 \\
	20	+ & (0.079) & (0.079) & (0.080) & (0.078) & (0.079) & (0.080) & (0.113) & (0.135) & (0.135) \\
	21	+log_accommodates & -0.997 & -1.060 & -1.327 & -0.522 & -0.312 & -0.313 & -0.657 & -1.094 & -1.094 \\
	22	+ & (0.824) & (0.829) & (0.797) & (0.768) & (0.778) & (0.779) & (0.847) & (1.135) & (1.135) \\
	23	+log_bedrooms & -1.429* & -1.373* & -1.433* & -1.272* & -1.283* & -1.281* & -1.299* & -2.053 & -2.053 \\
	24	+ & (0.686) & (0.691) & (0.654) & (0.613) & (0.627) & (0.628) & (0.657) & (0.774) & (0.774) \\
	25	+log_bathrooms & 0.497 & 0.613 & 0.622 & 0.737 & 0.702 & 0.706 & 0.523 & 0.645 & 0.645 \\
	26	+ & (0.426) & (0.433) & (0.414) & (0.404) & (0.410) & (0.410) & (0.426) & (0.541) & (0.541) \\
	27	+bathrooms_per_guest & -0.136 & -0.201 & -0.244 & -0.401 & -0.364 & -0.366 & -0.445* & -0.441 & -0.441 \\
	28	+ & (0.201) & (0.204) & (0.195) & (0.224) & (0.229) & (0.229) & (0.225) & (0.335) & (0.335) \\
	29	+bedrooms_per_guest & 0.008 & 0.011 & -0.015 & -0.096 & -0.091 & -0.090 & -0.117 & 0.018 & 0.018 \\
	30	+ & (0.432) & (0.436) & (0.401) & (0.346) & (0.360) & (0.362) & (0.361) & (0.529) & (0.529) \\
	31	+large_capacity_listing & 0.035 & 0.033 & -0.028 & 0.040 & 0.032 & 0.033 & 0.050 & -0.125 & -0.125 \\
	32	+ & (0.115) & (0.116) & (0.114) & (0.115) & (0.114) & (0.114) & (0.113) & (0.148) & (0.148) \\
	33	+accommodates_sq & -0.002 & -0.003 & -0.007 & -0.001 & 0.000 & 0.000 & -0.001 & -0.005 & -0.005 \\
	34	+ & (0.006) & (0.006) & (0.006) & (0.006) & (0.006) & (0.006) & (0.006) & (0.008) & (0.008) \\
	35	+bedrooms_sq & -0.048* & -0.048* & -0.047** & -0.040* & -0.039* & -0.039* & -0.036* & -0.063 & -0.063 \\
	36	+ & (0.020) & (0.021) & (0.018) & (0.017) & (0.018) & (0.018) & (0.017) & (0.020) & (0.020) \\
	37	+bathrooms_sq & 0.014 & 0.015 & 0.014 & 0.014 & 0.014 & 0.014 & 0.008 & 0.028* & 0.028* \\
	38	+ & (0.015) & (0.016) & (0.014) & (0.012) & (0.013) & (0.013) & (0.013) & (0.013) & (0.013) \\
	39	+amenities_count & & 0.005 & 0.009 & 0.003 & 0.003 & 0.003 & 0.006 & 0.003 & 0.003 \\
	40	+ & & (0.010) & (0.010) & (0.010) & (0.010) & (0.010) & (0.010) & (0.013) & (0.013) \\
	41	+amenity_richness_index & & -0.189* & -0.191* & -0.129 & -0.340* & -0.343* & -0.306* & -0.269* & -0.269* \\
	42	+ & & (0.017) & (0.016) & (0.046) & (0.083) & (0.084) & (0.087) & (0.106) & (0.106) \\
	43	+luxury_index & & 0.437 & 0.342 & 0.507 & 0.473 & 0.478 & 0.380 & 0.445 & 0.445 \\
	44	+ & & (0.474) & (0.468) & (0.461) & (0.458) & (0.458) & (0.452) & (0.571) & (0.571) \\
	45	+business_travel_index & & -0.761 & -0.824 & -0.836 & -0.799 & -0.804 & -0.801 & -1.156* & -1.156* \\
	46	+ & & (0.463) & (0.451) & (0.465) & (0.462) & (0.462) & (0.456) & (0.560) & (0.560) \\
	47	+family_comfort_index & & -0.257 & 0.014 & 0.368 & 0.313 & 0.308 & 0.300 & 0.921 & 0.921 \\
	48	+ & & (0.490) & (0.480) & (0.489) & (0.483) & (0.483) & (0.478) & (0.588) & (0.588) \\
	49	+leisure_index & & -0.132 & -0.035 & 0.236 & 0.265 & 0.264 & 0.203 & 0.027 & 0.027 \\
	50	+ & & (0.481) & (0.470) & (0.466) & (0.464) & (0.464) & (0.461) & (0.561) & (0.561) \\
	51	+remote_work_index & & -0.263 & -0.252 & -0.394 & -0.312 & -0.308 & -0.408 & -0.090 & -0.090 \\
	52	+ & & (0.486) & (0.479) & (0.485) & (0.484) & (0.484) & (0.478) & (0.568) & (0.568) \\
	53	+pets_allowed & & 0.129 & 0.069 & 0.016 & 0.006 & 0.007 & 0.029 & 0.023 & 0.023 \\
	54	+ & & (0.076) & (0.075) & (0.076) & (0.075) & (0.075) & (0.074) & (0.093) & (0.093) \\
	55	+is_superhost & & & -0.094* & -0.147 & -0.152 & -0.151 & -0.114 & 0.039 & 0.039 \\
	56	+ & & & (0.037) & (0.100) & (0.099) & (0.099) & (0.095) & (0.105) & (0.105) \\
	57	+superhost_flag & & & -0.094* & -0.147 & -0.152 & -0.151 & -0.114 & 0.039 & 0.039 \\
	58	+ & & & (0.037) & (0.100) & (0.099) & (0.099) & (0.095) & (0.105) & (0.105) \\
	59	+host_professional_dummy & & & -0.000 & -0.000 & 0.000* & 0.000* & 0.000*** & 0.000 & 0.000 \\
	60	+ & & & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) \\
	61	+multi_listing_host & & & 0.346* & 0.273* & 0.242* & 0.244* & -0.025 & 0.002 & 0.002 \\
	62	+ & & & (0.068) & (0.070) & (0.069) & (0.070) & (0.078) & (0.078) & (0.078) \\
	63	+superhost_x_entire_home & & & -0.177 & -0.028 & -0.037 & -0.037 & -0.017 & -0.009 & -0.009 \\
	64	+ & & & (0.096) & (0.102) & (0.101) & (0.101) & (0.100) & (0.121) & (0.121) \\
	65	+guest_favorite_flag & & & -0.194*** & -0.204* & -0.197* & -0.197* & -0.142 & -0.012 & -0.012 \\
	66	+ & & & (0.026) & (0.098) & (0.097) & (0.097) & (0.093) & (0.102) & (0.102) \\
	67	+is_guest_favorite & & & -0.194*** & -0.204* & -0.197* & -0.197* & -0.142 & -0.012 & -0.012 \\
	68	+ & & & (0.026) & (0.098) & (0.097) & (0.097) & (0.093) & (0.102) & (0.102) \\
	69	+rating & & & & 0.591 & 0.616 & 0.618 & 0.605 & 1.052* & 1.052* \\
	70	+ & & & & (0.526) & (0.520) & (0.521) & (0.515) & (0.494) & (0.494) \\
	71	+num_reviews & & & & 0.002 & 0.002 & 0.002 & 0.002 & 0.003 & 0.003 \\
	72	+ & & & & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) \\
	73	+log_reviews & & & & -0.518* & -0.521* & -0.521* & -0.452* & -0.400* & -0.400* \\
	74	+ & & & & (0.055) & (0.055) & (0.055) & (0.056) & (0.066) & (0.066) \\
	75	+review_quality & & & & -0.169 & -0.171 & -0.172 & -0.177 & -0.165 & -0.165 \\
	76	+ & & & & (0.135) & (0.134) & (0.134) & (0.136) & (0.135) & (0.135) \\
	77	+reputation_score & & & & 0.041 & 0.044 & 0.044 & 0.046 & 0.103 & 0.103 \\
	78	+ & & & & (0.064) & (0.063) & (0.064) & (0.063) & (0.061) & (0.061) \\
	79	+cleanliness_premium & & & & 0.267 & 0.268 & 0.268 & 0.283 & 0.207 & 0.207 \\
	80	+ & & & & (0.098) & (0.097) & (0.098) & (0.097) & (0.110) & (0.110) \\
	81	+location_rating_premium & & & & -0.047 & -0.045 & -0.044 & -0.148 & -0.146 & -0.146 \\
	82	+ & & & & (0.099) & (0.098) & (0.099) & (0.098) & (0.116) & (0.116) \\
	83	+has_reviews & & & & 4.556 & 11.999* & 12.118* & 10.831* & 9.511* & 9.511* \\
	84	+ & & & & (1.619) & (2.929) & (2.961) & (3.085) & (3.738) & (3.738) \\
	85	+quality_score & & & & 0.010 & 0.010 & 0.010 & 0.004 & -0.017 & -0.017 \\
	86	+ & & & & (0.018) & (0.018) & (0.018) & (0.017) & (0.018) & (0.018) \\
	87	+distance_to_center & & & & & -0.000 & -0.000 & -0.000 & -0.000 & -0.000 \\
	88	+ & & & & & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) \\
	89	+lat & & & & & -0.153* & -0.155* & -0.095** & -0.090* & -0.090* \\
	90	+ & & & & & (0.035) & (0.035) & (0.037) & (0.045) & (0.045) \\
	91	+long & & & & & 0.008 & 0.008 & 0.009 & 0.004 & 0.004 \\
	92	+ & & & & & (0.016) & (0.016) & (0.016) & (0.019) & (0.019) \\
	93	+spatial_cluster & & & & & & -0.001 & -0.014* & -0.013 & -0.013** \\
	94	+ & & & & & & (0.004) & (0.004) & (0.005) & (0.005) \\
	95	+title_length & & & & & & & 0.010* & 0.007 & 0.007 \\
	96	+ & & & & & & & (0.004) & (0.006) & (0.006) \\
	97	+title_word_count & & & & & & & -0.094* & -0.058 & -0.058 \\
	98	+ & & & & & & & (0.042) & (0.053) & (0.053) \\
	99	+luxury_language_score & & & & & & & -0.239 & 0.218 & 0.218 \\
	100	+ & & & & & & & (0.351) & (196.632) & (196.632) \\
	101	+cozy_language_score & & & & & & & 0.000 & 0.000 & 0.000 \\
	102	+ & & & & & & & (0.000) & (0.000) & (0.000) \\
	103	+professional_tone_score & & & & & & & -0.173 & -0.623 & -0.623 \\
	104	+ & & & & & & & (0.414) & (19.507) & (19.507) \\
	105	+tourism_language_score & & & & & & & -0.112 & -0.209 & -0.209 \\
	106	+ & & & & & & & (0.099) & (0.115) & (0.115) \\
	107	+text_embed_0 & & & & & & & 3.000* & 2.800* & 2.800*** \\
	108	+ & & & & & & & (0.353) & (0.430) & (0.430) \\
	109	+text_embed_1 & & & & & & & 0.247 & 0.479 & 0.479 \\
	110	+ & & & & & & & (0.366) & (0.442) & (0.442) \\
	111	+text_embed_2 & & & & & & & -0.400 & -0.085 & -0.085 \\
	112	+ & & & & & & & (0.395) & (0.460) & (0.460) \\
	113	+text_embed_3 & & & & & & & -0.035 & -0.055 & -0.055 \\
	114	+ & & & & & & & (0.218) & (0.244) & (0.244) \\
	115	+local_density_500m & & & & & & & & -0.008 & -0.008 \\
	116	+ & & & & & & & & (0.003) & (0.003) \\
	117	+local_density_1km & & & & & & & & 0.003 & 0.003 \\
	118	+ & & & & & & & & (0.001) & (0.001) \\
	119	+local_avg_price_500m & & & & & & & & 0.000* & 0.000* \\
	120	+ & & & & & & & & (0.000) & (0.000) \\
	121	+local_avg_price_1km & & & & & & & & -0.000 & -0.000 \\
	122	+ & & & & & & & & (0.000) & (0.000) \\
	123	+N & 3412 & 3412 & 3412 & 2861 & 2857 & 2857 & 2857 & 1906 & 1906 \\
	124	+R2 & 0.0516 & 0.0575 & 0.0985 & 0.1871 & 0.1979 & 0.1980 & 0.2249 & 0.2735 & 0.2735 \\
	125	+Adj_R2 & 0.0477 & 0.0516 & 0.0918 & 0.1779 & 0.1880 & 0.1877 & 0.2125 & 0.2544 & 0.2544 \\
	126	+AIC & 11911.4 & 11904.2 & 11760.5 & 9381.7 & 9336.4 & 9338.3 & 9258.6 & 6103.0 & 6103.0 \\
	127	+BIC & 12003.4 & 12039.2 & 11920.0 & 9578.3 & 9550.9 & 9558.8 & 9532.6 & 6380.6 & 6380.6 \\
	128	+\bottomrule
	129	+\end{tabular}
	130	+\par\smallskip\footnotesize
	131	+\textit{Note:} $^{*}p<0.001$, $^{}p<0.01$, $^{*}p<0.05$.
	132	+\end{table}
		\ No newline at end of file

added scripts/app.py +922 −0

		`@@ -0,0 +1,922 @@`
	1	+"""
	2	+Airbnb Hedonic Pricing Lab
	3	+==========================
	4	+Interactive Streamlit dashboard for hedonic pricing analysis of Quebec Airbnb listings.
	5	+"""
	6	+
	7	+import streamlit as st
	8	+import pandas as pd
	9	+import numpy as np
	10	+import plotly.express as px
	11	+import plotly.graph_objects as go
	12	+import matplotlib
	13	+matplotlib.use("Agg")
	14	+import matplotlib.pyplot as plt
	15	+import warnings
	16	+import os
	17	+import joblib
	18	+
	19	+warnings.filterwarnings("ignore")
	20	+
	21	+# ---------------------------------------------------------------------------
	22	+# Page config
	23	+# ---------------------------------------------------------------------------
	24	+st.set_page_config(
	25	+ page_title="Airbnb Hedonic Pricing Lab",
	26	+ page_icon="🏠",
	27	+ layout="wide",
	28	+ initial_sidebar_state="expanded",
	29	+)
	30	+
	31	+DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data", "raw", "cleaned_dataset_sample.csv")
	32	+MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "results", "models")
	33	+
	34	+# ---------------------------------------------------------------------------
	35	+# Custom CSS
	36	+# ---------------------------------------------------------------------------
	37	+st.markdown(
	38	+ """
	39	+ <style>
	40	+ .block-container {padding-top: 1.5rem; padding-bottom: 1rem;}
	41	+ h1 {color: #FF5A5F;}
	42	+ .stTabs [data-baseweb="tab-list"] {gap: 8px;}
	43	+ .stTabs [data-baseweb="tab"] {
	44	+ padding: 8px 20px;
	45	+ border-radius: 6px 6px 0 0;
	46	+ font-weight: 600;
	47	+ }
	48	+ .metric-card {
	49	+ background: #f8f9fa; border-radius: 10px; padding: 16px;
	50	+ text-align: center; border: 1px solid #e9ecef;
	51	+ }
	52	+ </style>
	53	+ """,
	54	+ unsafe_allow_html=True,
	55	+)
	56	+
	57	+# ---------------------------------------------------------------------------
	58	+# Data loading
	59	+# ---------------------------------------------------------------------------
	60	+@st.cache_data
	61	+def load_data():
	62	+ df = pd.read_csv(DATA_PATH)
	63	+ # Ensure boolean columns are truly boolean
	64	+ for col in ["is_superhost", "is_guest_favorite", "pets_allowed"]:
	65	+ df[col] = df[col].astype(bool)
	66	+ # Log price (for regression)
	67	+ df["log_price"] = np.log1p(df["price_numeric"])
	68	+ return df
	69	+
	70	+
	71	+raw_df = load_data()
	72	+
	73	+# ---------------------------------------------------------------------------
	74	+# Sidebar: global filters
	75	+# ---------------------------------------------------------------------------
	76	+st.sidebar.title("Airbnb Hedonic Pricing Lab")
	77	+st.sidebar.markdown("---")
	78	+st.sidebar.header("Global Filters")
	79	+
	80	+all_cities = sorted(raw_df["city"].dropna().unique())
	81	+top_cities = (
	82	+ raw_df["city"]
	83	+ .value_counts()
	84	+ .head(20)
	85	+ .index.tolist()
	86	+)
	87	+
	88	+selected_cities = st.sidebar.multiselect(
	89	+ "Cities",
	90	+ options=all_cities,
	91	+ default=top_cities[:5],
	92	+ help="Leave empty to include all cities.",
	93	+)
	94	+
	95	+all_property_types = sorted(raw_df["property_type"].dropna().unique())
	96	+selected_property_types = st.sidebar.multiselect(
	97	+ "Property Types",
	98	+ options=all_property_types,
	99	+ default=all_property_types,
	100	+)
	101	+
	102	+price_valid = raw_df["price_numeric"].dropna()
	103	+price_min_data = float(price_valid.min())
	104	+price_max_data = float(min(price_valid.max(), 10000)) # cap slider for UX
	105	+price_range = st.sidebar.slider(
	106	+ "Price Range ($)",
	107	+ min_value=0.0,
	108	+ max_value=price_max_data,
	109	+ value=(0.0, price_max_data),
	110	+ step=10.0,
	111	+)
	112	+
	113	+superhost_only = st.sidebar.checkbox("Superhost only", value=False)
	114	+
	115	+st.sidebar.markdown("---")
	116	+st.sidebar.caption(
	117	+ f"Dataset: {len(raw_df):,} listings across {raw_df['city'].nunique()} cities in Quebec."
	118	+)
	119	+
	120	+# ---------------------------------------------------------------------------
	121	+# Apply global filters
	122	+# ---------------------------------------------------------------------------
	123	+def apply_global_filters(df: pd.DataFrame) -> pd.DataFrame:
	124	+ mask = pd.Series(True, index=df.index)
	125	+ if selected_cities:
	126	+ mask &= df["city"].isin(selected_cities)
	127	+ if selected_property_types:
	128	+ mask &= df["property_type"].isin(selected_property_types)
	129	+ mask &= (df["price_numeric"] >= price_range[0]) \| df["price_numeric"].isna()
	130	+ mask &= (df["price_numeric"] <= price_range[1]) \| df["price_numeric"].isna()
	131	+ if superhost_only:
	132	+ mask &= df["is_superhost"]
	133	+ return df.loc[mask].copy()
	134	+
	135	+
	136	+df = apply_global_filters(raw_df)
	137	+
	138	+# ---------------------------------------------------------------------------
	139	+# Hedonic model helpers
	140	+# ---------------------------------------------------------------------------
	141	+FEATURE_COLS = [
	142	+ "bedrooms",
	143	+ "bathrooms",
	144	+ "guests_count",
	145	+ "amenities_count",
	146	+ "num_images",
	147	+ "rating",
	148	+ "is_superhost",
	149	+ "is_guest_favorite",
	150	+ "pets_allowed",
	151	+]
	152	+
	153	+NUMERIC_FEATURES = [
	154	+ "bedrooms",
	155	+ "bathrooms",
	156	+ "guests_count",
	157	+ "amenities_count",
	158	+ "num_images",
	159	+ "rating",
	160	+]
	161	+
	162	+BOOL_FEATURES = ["is_superhost", "is_guest_favorite", "pets_allowed"]
	163	+
	164	+
	165	+def _prepare_model_df(data: pd.DataFrame) -> pd.DataFrame:
	166	+ """Return a clean DataFrame ready for modelling (no NaN in target/features)."""
	167	+ mdf = data.dropna(subset=["price_numeric"]).copy()
	168	+ mdf = mdf[mdf["price_numeric"] > 0].copy()
	169	+ mdf["log_price"] = np.log(mdf["price_numeric"])
	170	+ for col in BOOL_FEATURES:
	171	+ mdf[col] = mdf[col].astype(int)
	172	+ # City dummies (top cities)
	173	+ city_counts = mdf["city"].value_counts()
	174	+ top = city_counts[city_counts >= 20].index.tolist()
	175	+ for c in top:
	176	+ mdf[f"city_{c}"] = (mdf["city"] == c).astype(int)
	177	+ # Property type dummies
	178	+ for pt in mdf["property_type"].unique():
	179	+ mdf[f"ptype_{pt}"] = (mdf["property_type"] == pt).astype(int)
	180	+ # Fill remaining NaN in numeric features with median
	181	+ for col in NUMERIC_FEATURES:
	182	+ mdf[col] = mdf[col].fillna(mdf[col].median())
	183	+ return mdf
	184	+
	185	+
	186	+@st.cache_resource
	187	+def fit_ols_model():
	188	+ """Fit an OLS hedonic model on the full raw dataset."""
	189	+ from sklearn.linear_model import LinearRegression
	190	+
	191	+ mdf = _prepare_model_df(raw_df)
	192	+ feature_names = list(FEATURE_COLS)
	193	+ city_cols = [c for c in mdf.columns if c.startswith("city_")]
	194	+ ptype_cols = [c for c in mdf.columns if c.startswith("ptype_")]
	195	+ feature_names = FEATURE_COLS + city_cols + ptype_cols
	196	+ X = mdf[feature_names].values
	197	+ y = mdf["log_price"].values
	198	+ model = LinearRegression()
	199	+ model.fit(X, y)
	200	+ return model, feature_names, mdf
	201	+
	202	+
	203	+@st.cache_resource
	204	+def fit_gbm_model():
	205	+ """Fit a gradient-boosted model for prediction & SHAP."""
	206	+ try:
	207	+ from sklearn.ensemble import GradientBoostingRegressor
	208	+ except ImportError:
	209	+ return None, None, None
	210	+
	211	+ mdf = _prepare_model_df(raw_df)
	212	+ feature_names = list(FEATURE_COLS)
	213	+ city_cols = sorted([c for c in mdf.columns if c.startswith("city_")])
	214	+ ptype_cols = sorted([c for c in mdf.columns if c.startswith("ptype_")])
	215	+ feature_names = FEATURE_COLS + city_cols + ptype_cols
	216	+ X = mdf[feature_names].values
	217	+ y = mdf["log_price"].values
	218	+ model = GradientBoostingRegressor(
	219	+ n_estimators=200,
	220	+ max_depth=5,
	221	+ learning_rate=0.1,
	222	+ subsample=0.8,
	223	+ random_state=42,
	224	+ )
	225	+ model.fit(X, y)
	226	+ return model, feature_names, mdf
	227	+
	228	+
	229	+# ---------------------------------------------------------------------------
	230	+# TABS
	231	+# ---------------------------------------------------------------------------
	232	+tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(
	233	+ [
	234	+ "Data Explorer",
	235	+ "Price Map",
	236	+ "Neighbourhood Comparison",
	237	+ "Hedonic Premiums",
	238	+ "Price Predictor",
	239	+ "SHAP Explorer",
	240	+ "Price Decomposition",
	241	+ ]
	242	+)
	243	+
	244	+# ===================================================================
	245	+# TAB 1 - Data Explorer
	246	+# ===================================================================
	247	+with tab1:
	248	+ st.header("Data Explorer")
	249	+
	250	+ # Summary metrics
	251	+ col1, col2, col3, col4, col5 = st.columns(5)
	252	+ with col1:
	253	+ st.metric("Listings", f"{len(df):,}")
	254	+ with col2:
	255	+ st.metric("Cities", f"{df['city'].nunique()}")
	256	+ with col3:
	257	+ median_price = df["price_numeric"].median()
	258	+ st.metric("Median Price", f"${median_price:,.0f}" if pd.notna(median_price) else "N/A")
	259	+ with col4:
	260	+ avg_rating = df["rating"].mean()
	261	+ st.metric("Avg Rating", f"{avg_rating:.2f}" if pd.notna(avg_rating) else "N/A")
	262	+ with col5:
	263	+ superhost_pct = df["is_superhost"].mean() * 100
	264	+ st.metric("Superhosts", f"{superhost_pct:.1f}%")
	265	+
	266	+ st.markdown("---")
	267	+
	268	+ # Summary statistics table
	269	+ st.subheader("Summary Statistics")
	270	+ numeric_cols = [
	271	+ "price_numeric", "rating", "bedrooms", "bathrooms",
	272	+ "guests_count", "amenities_count", "num_images", "num_reviews", "quality_score",
	273	+ ]
	274	+ summary = df[numeric_cols].describe().T
	275	+ summary.columns = ["Count", "Mean", "Std", "Min", "25%", "50%", "75%", "Max"]
	276	+ st.dataframe(summary.style.format("{:.2f}"), use_container_width=True)
	277	+
	278	+ st.markdown("---")
	279	+
	280	+ # Price distribution
	281	+ st.subheader("Price Distribution")
	282	+ price_data = df["price_numeric"].dropna()
	283	+ if len(price_data) > 0:
	284	+ cap = st.slider(
	285	+ "Upper price cap for histogram",
	286	+ min_value=100,
	287	+ max_value=5000,
	288	+ value=1500,
	289	+ step=50,
	290	+ key="price_hist_cap",
	291	+ )
	292	+ fig_hist = px.histogram(
	293	+ price_data[price_data <= cap],
	294	+ nbins=60,
	295	+ title="Price Distribution (capped)",
	296	+ labels={"value": "Price ($)", "count": "Frequency"},
	297	+ color_discrete_sequence=["#FF5A5F"],
	298	+ )
	299	+ fig_hist.update_layout(
	300	+ showlegend=False,
	301	+ xaxis_title="Price ($)",
	302	+ yaxis_title="Count",
	303	+ template="plotly_white",
	304	+ )
	305	+ st.plotly_chart(fig_hist, use_container_width=True)
	306	+ else:
	307	+ st.info("No price data available for the current filter selection.")
	308	+
	309	+ st.markdown("---")
	310	+
	311	+ # Filtered data table
	312	+ st.subheader("Filtered Data")
	313	+ display_cols = [
	314	+ "name", "city", "property_type", "price_numeric", "rating",
	315	+ "bedrooms", "bathrooms", "guests_count", "amenities_count",
	316	+ "is_superhost", "pets_allowed",
	317	+ ]
	318	+ st.dataframe(
	319	+ df[display_cols].reset_index(drop=True),
	320	+ use_container_width=True,
	321	+ height=400,
	322	+ )
	323	+
	324	+# ===================================================================
	325	+# TAB 2 - Price Map
	326	+# ===================================================================
	327	+with tab2:
	328	+ st.header("Price Map")
	329	+
	330	+ map_df = df.dropna(subset=["price_numeric", "lat", "long"]).copy()
	331	+ map_df = map_df[(map_df["lat"] != 0) & (map_df["long"] != 0)]
	332	+
	333	+ if len(map_df) == 0:
	334	+ st.warning("No geolocated listings with price data in current filter.")
	335	+ else:
	336	+ map_col1, map_col2 = st.columns([1, 3])
	337	+ with map_col1:
	338	+ color_by = st.radio(
	339	+ "Color by", ["price_numeric", "log_price", "rating"], index=0, key="map_color"
	340	+ )
	341	+ map_price_cap = st.slider(
	342	+ "Price cap for map color scale",
	343	+ 100, 5000, 2000, 100,
	344	+ key="map_cap",
	345	+ )
	346	+
	347	+ map_plot = map_df.copy()
	348	+ if color_by == "price_numeric":
	349	+ map_plot["color_val"] = map_plot["price_numeric"].clip(upper=map_price_cap)
	350	+ elif color_by == "log_price":
	351	+ map_plot["color_val"] = map_plot["log_price"]
	352	+ else:
	353	+ map_plot["color_val"] = map_plot["rating"]
	354	+
	355	+ map_plot["size_val"] = map_plot["guests_count"].clip(lower=1, upper=16)
	356	+
	357	+ with map_col2:
	358	+ fig_map = px.scatter_map(
	359	+ map_plot,
	360	+ lat="lat",
	361	+ lon="long",
	362	+ color="color_val",
	363	+ size="size_val",
	364	+ color_continuous_scale="RdYlGn_r" if "price" in color_by else "Viridis",
	365	+ size_max=14,
	366	+ zoom=5,
	367	+ hover_name="name",
	368	+ hover_data={
	369	+ "city": True,
	370	+ "property_type": True,
	371	+ "price_numeric": ":.0f",
	372	+ "rating": ":.2f",
	373	+ "guests_count": True,
	374	+ "color_val": False,
	375	+ "size_val": False,
	376	+ "lat": False,
	377	+ "long": False,
	378	+ },
	379	+ map_style="carto-positron",
	380	+ title="Listings Map",
	381	+ height=650,
	382	+ )
	383	+ fig_map.update_layout(
	384	+ coloraxis_colorbar_title=color_by.replace("_", " ").title(),
	385	+ margin=dict(l=0, r=0, t=40, b=0),
	386	+ )
	387	+ st.plotly_chart(fig_map, use_container_width=True)
	388	+
	389	+# ===================================================================
	390	+# TAB 3 - Neighbourhood Comparison
	391	+# ===================================================================
	392	+with tab3:
	393	+ st.header("Neighbourhood Comparison")
	394	+
	395	+ compare_cities = st.multiselect(
	396	+ "Select cities to compare",
	397	+ options=all_cities,
	398	+ default=top_cities[:8],
	399	+ key="compare_cities",
	400	+ )
	401	+
	402	+ if not compare_cities:
	403	+ st.info("Please select at least one city to compare.")
	404	+ else:
	405	+ cdf = raw_df[raw_df["city"].isin(compare_cities)].dropna(subset=["price_numeric"])
	406	+ cdf = cdf[cdf["price_numeric"] > 0]
	407	+
	408	+ if len(cdf) == 0:
	409	+ st.warning("No price data for the selected cities.")
	410	+ else:
	411	+ c1, c2 = st.columns(2)
	412	+
	413	+ with c1:
	414	+ st.subheader("Price Distribution by City")
	415	+ fig_box = px.box(
	416	+ cdf,
	417	+ x="city",
	418	+ y="price_numeric",
	419	+ color="city",
	420	+ title="Price Box Plot by City",
	421	+ labels={"price_numeric": "Price ($)", "city": "City"},
	422	+ color_discrete_sequence=px.colors.qualitative.Set2,
	423	+ )
	424	+ fig_box.update_layout(
	425	+ showlegend=False,
	426	+ template="plotly_white",
	427	+ yaxis=dict(range=[0, cdf["price_numeric"].quantile(0.95)]),
	428	+ )
	429	+ st.plotly_chart(fig_box, use_container_width=True)
	430	+
	431	+ with c2:
	432	+ st.subheader("Median Price by City")
	433	+ city_med = (
	434	+ cdf.groupby("city")["price_numeric"]
	435	+ .median()
	436	+ .sort_values(ascending=False)
	437	+ .reset_index()
	438	+ )
	439	+ city_med.columns = ["City", "Median Price"]
	440	+ fig_bar = px.bar(
	441	+ city_med,
	442	+ x="Median Price",
	443	+ y="City",
	444	+ orientation="h",
	445	+ title="Median Nightly Price by City",
	446	+ color="Median Price",
	447	+ color_continuous_scale="Sunset",
	448	+ )
	449	+ fig_bar.update_layout(template="plotly_white", yaxis=dict(autorange="reversed"))
	450	+ st.plotly_chart(fig_bar, use_container_width=True)
	451	+
	452	+ # Summary table
	453	+ st.subheader("City Summary Statistics")
	454	+ city_stats = (
	455	+ cdf.groupby("city")
	456	+ .agg(
	457	+ Listings=("price_numeric", "count"),
	458	+ Mean_Price=("price_numeric", "mean"),
	459	+ Median_Price=("price_numeric", "median"),
	460	+ Std_Price=("price_numeric", "std"),
	461	+ Avg_Rating=("rating", "mean"),
	462	+ Avg_Bedrooms=("bedrooms", "mean"),
	463	+ Superhost_Pct=("is_superhost", "mean"),
	464	+ )
	465	+ .round(2)
	466	+ )
	467	+ city_stats["Superhost_Pct"] = (city_stats["Superhost_Pct"] * 100).round(1)
	468	+ st.dataframe(city_stats.sort_values("Median_Price", ascending=False), use_container_width=True)
	469	+
	470	+# ===================================================================
	471	+# TAB 4 - Hedonic Premiums (OLS)
	472	+# ===================================================================
	473	+with tab4:
	474	+ st.header("Hedonic Premiums (OLS Regression)")
	475	+ st.markdown(
	476	+ """
	477	+ A semi-log hedonic model is estimated: *ln(Price) = X beta + epsilon**.
	478	+ Coefficients are converted to percentage premiums via (exp(beta) - 1) x 100.
	479	+ """
	480	+ )
	481	+
	482	+ with st.spinner("Fitting OLS hedonic model..."):
	483	+ ols_model, ols_features, ols_mdf = fit_ols_model()
	484	+
	485	+ coefs = pd.DataFrame(
	486	+ {"Feature": ols_features, "Coefficient": ols_model.coef_}
	487	+ )
	488	+ coefs["Premium (%)"] = (np.exp(coefs["Coefficient"]) - 1) * 100
	489	+ coefs = coefs.sort_values("Premium (%)", ascending=False).reset_index(drop=True)
	490	+
	491	+ c1, c2 = st.columns([1, 2])
	492	+
	493	+ with c1:
	494	+ st.subheader("Coefficient Table")
	495	+ st.dataframe(
	496	+ coefs.style.format({"Coefficient": "{:.4f}", "Premium (%)": "{:.2f}"}),
	497	+ use_container_width=True,
	498	+ height=500,
	499	+ )
	500	+
	501	+ r2 = ols_model.score(
	502	+ ols_mdf[ols_features].values, ols_mdf["log_price"].values
	503	+ )
	504	+ st.metric("R-squared", f"{r2:.4f}")
	505	+ st.metric("N observations", f"{len(ols_mdf):,}")
	506	+
	507	+ with c2:
	508	+ st.subheader("Hedonic Premiums: Key Features")
	509	+ key_features = FEATURE_COLS + [
	510	+ f for f in ols_features if f.startswith("ptype_")
	511	+ ]
	512	+ key_coefs = coefs[coefs["Feature"].isin(key_features)].sort_values("Premium (%)")
	513	+
	514	+ fig_prem = px.bar(
	515	+ key_coefs,
	516	+ x="Premium (%)",
	517	+ y="Feature",
	518	+ orientation="h",
	519	+ color="Premium (%)",
	520	+ color_continuous_scale="RdYlGn",
	521	+ title="Estimated Price Premiums (%)",
	522	+ height=500,
	523	+ )
	524	+ fig_prem.update_layout(template="plotly_white")
	525	+ st.plotly_chart(fig_prem, use_container_width=True)
	526	+
	527	+ st.markdown("---")
	528	+ st.subheader("Interpretation")
	529	+ st.markdown(
	530	+ """
	531	+ - Positive premiums indicate features that command a higher nightly price.
	532	+ - Negative premiums suggest a discount relative to the baseline.
	533	+ - For continuous variables (e.g., bedrooms), the premium is per unit increase.
	534	+ - For binary variables (e.g., is_superhost), the premium is relative to a non-superhost listing.
	535	+ - City and property-type dummies capture location and structural fixed effects.
	536	+ """
	537	+ )
	538	+
	539	+# ===================================================================
	540	+# TAB 5 - Price Predictor
	541	+# ===================================================================
	542	+with tab5:
	543	+ st.header("Price Predictor")
	544	+ st.markdown("Adjust the inputs below to predict the nightly Airbnb price.")
	545	+
	546	+ with st.spinner("Loading prediction model..."):
	547	+ gbm_model, gbm_features, gbm_mdf = fit_gbm_model()
	548	+
	549	+ if gbm_model is None:
	550	+ st.error("Could not fit prediction model. Check dependencies.")
	551	+ else:
	552	+ pred_c1, pred_c2 = st.columns([1, 2])
	553	+
	554	+ with pred_c1:
	555	+ st.subheader("Listing Features")
	556	+ pred_city = st.selectbox("City", options=top_cities[:20], index=0, key="pred_city")
	557	+ pred_ptype = st.selectbox("Property Type", options=all_property_types, index=0, key="pred_ptype")
	558	+ pred_bedrooms = st.slider("Bedrooms", 1, 10, 2, key="pred_bed")
	559	+ pred_bathrooms = st.slider("Bathrooms", 0.0, 5.0, 1.0, 0.5, key="pred_bath")
	560	+ pred_guests = st.slider("Guests", 1, 16, 4, key="pred_guests")
	561	+ pred_amenities = st.slider("Amenities count", 0, 100, 30, key="pred_amen")
	562	+ pred_images = st.slider("Number of images", 1, 50, 15, key="pred_img")
	563	+ pred_rating = st.slider("Rating", 0.0, 5.0, 4.8, 0.01, key="pred_rat")
	564	+ pred_superhost = st.checkbox("Superhost", value=False, key="pred_sh")
	565	+ pred_fav = st.checkbox("Guest Favorite", value=False, key="pred_fav")
	566	+ pred_pets = st.checkbox("Pets Allowed", value=False, key="pred_pets")
	567	+
	568	+ # Build feature vector
	569	+ input_dict = {
	570	+ "bedrooms": pred_bedrooms,
	571	+ "bathrooms": pred_bathrooms,
	572	+ "guests_count": pred_guests,
	573	+ "amenities_count": pred_amenities,
	574	+ "num_images": pred_images,
	575	+ "rating": pred_rating,
	576	+ "is_superhost": int(pred_superhost),
	577	+ "is_guest_favorite": int(pred_fav),
	578	+ "pets_allowed": int(pred_pets),
	579	+ }
	580	+ # City dummies
	581	+ city_cols = [f for f in gbm_features if f.startswith("city_")]
	582	+ for c in city_cols:
	583	+ input_dict[c] = 1 if c == f"city_{pred_city}" else 0
	584	+ # Property type dummies
	585	+ ptype_cols = [f for f in gbm_features if f.startswith("ptype_")]
	586	+ for p in ptype_cols:
	587	+ input_dict[p] = 1 if p == f"ptype_{pred_ptype}" else 0
	588	+
	589	+ X_pred = np.array([[input_dict.get(f, 0) for f in gbm_features]])
	590	+ log_pred = gbm_model.predict(X_pred)[0]
	591	+ pred_price = np.exp(log_pred)
	592	+
	593	+ # Approximate confidence interval via training residuals
	594	+ y_train = gbm_mdf["log_price"].values
	595	+ y_hat_train = gbm_model.predict(gbm_mdf[gbm_features].values)
	596	+ residual_std = np.std(y_train - y_hat_train)
	597	+ ci_low = np.exp(log_pred - 1.96 * residual_std)
	598	+ ci_high = np.exp(log_pred + 1.96 * residual_std)
	599	+
	600	+ with pred_c2:
	601	+ st.subheader("Predicted Price")
	602	+
	603	+ m1, m2, m3 = st.columns(3)
	604	+ with m1:
	605	+ st.metric("Lower 95% CI", f"${ci_low:,.0f}")
	606	+ with m2:
	607	+ st.metric("Predicted Price", f"${pred_price:,.0f}")
	608	+ with m3:
	609	+ st.metric("Upper 95% CI", f"${ci_high:,.0f}")
	610	+
	611	+ # Feature contribution breakdown
	612	+ st.subheader("Feature Contribution Breakdown")
	613	+ st.markdown(
	614	+ "Approximate marginal contribution of each feature group "
	615	+ "(setting each to its baseline and measuring the price change)."
	616	+ )
	617	+
	618	+ # Compute contributions by zeroing out each feature
	619	+ baseline_dict = {}
	620	+ for f in gbm_features:
	621	+ baseline_dict[f] = gbm_mdf[f].median() if f in gbm_mdf.columns else 0
	622	+ X_base = np.array([[baseline_dict.get(f, 0) for f in gbm_features]])
	623	+ base_log = gbm_model.predict(X_base)[0]
	624	+
	625	+ contributions = {}
	626	+ feature_groups = {
	627	+ "Bedrooms": ["bedrooms"],
	628	+ "Bathrooms": ["bathrooms"],
	629	+ "Guests": ["guests_count"],
	630	+ "Amenities": ["amenities_count"],
	631	+ "Images": ["num_images"],
	632	+ "Rating": ["rating"],
	633	+ "Superhost": ["is_superhost"],
	634	+ "Guest Favorite": ["is_guest_favorite"],
	635	+ "Pets Allowed": ["pets_allowed"],
	636	+ "City": city_cols,
	637	+ "Property Type": ptype_cols,
	638	+ }
	639	+
	640	+ for group_name, cols in feature_groups.items():
	641	+ X_modified = X_pred.copy()
	642	+ for col_name in cols:
	643	+ idx = gbm_features.index(col_name)
	644	+ X_modified[0, idx] = baseline_dict.get(col_name, 0)
	645	+ modified_log = gbm_model.predict(X_modified)[0]
	646	+ # Contribution = what the feature adds relative to baseline
	647	+ contributions[group_name] = (log_pred - modified_log) * pred_price
	648	+
	649	+ contrib_df = pd.DataFrame(
	650	+ {"Feature": list(contributions.keys()), "Contribution ($)": list(contributions.values())}
	651	+ ).sort_values("Contribution ($)")
	652	+
	653	+ fig_contrib = px.bar(
	654	+ contrib_df,
	655	+ x="Contribution ($)",
	656	+ y="Feature",
	657	+ orientation="h",
	658	+ color="Contribution ($)",
	659	+ color_continuous_scale="RdYlGn",
	660	+ title="Feature Contributions to Predicted Price",
	661	+ )
	662	+ fig_contrib.update_layout(template="plotly_white", height=420)
	663	+ st.plotly_chart(fig_contrib, use_container_width=True)
	664	+
	665	+# ===================================================================
	666	+# TAB 6 - SHAP Explorer
	667	+# ===================================================================
	668	+with tab6:
	669	+ st.header("SHAP Explorer")
	670	+
	671	+ shap_available = True
	672	+ try:
	673	+ import shap
	674	+ except ImportError:
	675	+ shap_available = False
	676	+
	677	+ if not shap_available:
	678	+ st.error(
	679	+ "The `shap` library is not installed. "
	680	+ "Install it with `pip install shap` to enable this tab."
	681	+ )
	682	+ else:
	683	+ with st.spinner("Computing SHAP values (this may take a moment)..."):
	684	+ gbm_model_s, gbm_features_s, gbm_mdf_s = fit_gbm_model()
	685	+
	686	+ if gbm_model_s is None:
	687	+ st.error("Could not load model for SHAP analysis.")
	688	+ else:
	689	+ @st.cache_data
	690	+ def compute_shap_values():
	691	+ _model, _features, _mdf = fit_gbm_model()
	692	+ X_shap = _mdf[_features].values
	693	+ # Use a sample if dataset is large
	694	+ sample_size = min(500, len(X_shap))
	695	+ rng = np.random.RandomState(42)
	696	+ idx = rng.choice(len(X_shap), sample_size, replace=False)
	697	+ X_sample = X_shap[idx]
	698	+ explainer = shap.TreeExplainer(_model)
	699	+ sv = explainer.shap_values(X_sample)
	700	+ return sv, X_sample, _features
	701	+
	702	+ shap_vals, X_shap_sample, shap_feature_names = compute_shap_values()
	703	+
	704	+ # SHAP Summary Plot
	705	+ st.subheader("SHAP Summary Plot")
	706	+ fig_shap_summary, ax_shap = plt.subplots(figsize=(10, 7))
	707	+ shap.summary_plot(
	708	+ shap_vals,
	709	+ X_shap_sample,
	710	+ feature_names=shap_feature_names,
	711	+ show=False,
	712	+ max_display=20,
	713	+ )
	714	+ plt.tight_layout()
	715	+ st.pyplot(fig_shap_summary)
	716	+ plt.close(fig_shap_summary)
	717	+
	718	+ # Feature importance ranking
	719	+ st.subheader("Feature Importance Ranking (Mean \|SHAP\|)")
	720	+ mean_abs_shap = np.abs(shap_vals).mean(axis=0)
	721	+ imp_df = pd.DataFrame(
	722	+ {"Feature": shap_feature_names, "Mean \|SHAP\|": mean_abs_shap}
	723	+ ).sort_values("Mean \|SHAP\|", ascending=False).head(20)
	724	+
	725	+ fig_imp = px.bar(
	726	+ imp_df,
	727	+ x="Mean \|SHAP\|",
	728	+ y="Feature",
	729	+ orientation="h",
	730	+ color="Mean \|SHAP\|",
	731	+ color_continuous_scale="Viridis",
	732	+ title="Top 20 Features by SHAP Importance",
	733	+ )
	734	+ fig_imp.update_layout(template="plotly_white", yaxis=dict(autorange="reversed"))
	735	+ st.plotly_chart(fig_imp, use_container_width=True)
	736	+
	737	+ # SHAP Dependence Plot
	738	+ st.subheader("SHAP Dependence Plot")
	739	+ dep_feature = st.selectbox(
	740	+ "Select feature for dependence plot",
	741	+ options=shap_feature_names,
	742	+ index=shap_feature_names.index("bedrooms") if "bedrooms" in shap_feature_names else 0,
	743	+ key="shap_dep_feature",
	744	+ )
	745	+ dep_idx = shap_feature_names.index(dep_feature)
	746	+ fig_dep, ax_dep = plt.subplots(figsize=(9, 5))
	747	+ shap.dependence_plot(
	748	+ dep_idx,
	749	+ shap_vals,
	750	+ X_shap_sample,
	751	+ feature_names=shap_feature_names,
	752	+ ax=ax_dep,
	753	+ show=False,
	754	+ )
	755	+ plt.tight_layout()
	756	+ st.pyplot(fig_dep)
	757	+ plt.close(fig_dep)
	758	+
	759	+# ===================================================================
	760	+# TAB 7 - Price Decomposition
	761	+# ===================================================================
	762	+with tab7:
	763	+ st.header("Price Decomposition")
	764	+ st.markdown("Select a listing to see its predicted price breakdown into component contributions.")
	765	+
	766	+ gbm_model_d, gbm_features_d, gbm_mdf_d = fit_gbm_model()
	767	+
	768	+ if gbm_model_d is None:
	769	+ st.error("Prediction model not available.")
	770	+ else:
	771	+ # Build listing selector from rows with valid prices
	772	+ decomp_df = raw_df.dropna(subset=["price_numeric"]).copy()
	773	+ decomp_df = decomp_df[decomp_df["price_numeric"] > 0]
	774	+ decomp_df["label"] = (
	775	+ decomp_df["name"].str[:60]
	776	+ + " \| "
	777	+ + decomp_df["city"]
	778	+ + " \| $"
	779	+ + decomp_df["price_numeric"].round(0).astype(int).astype(str)
	780	+ )
	781	+
	782	+ selected_label = st.selectbox(
	783	+ "Select a listing",
	784	+ options=decomp_df["label"].tolist()[:500],
	785	+ index=0,
	786	+ key="decomp_listing",
	787	+ )
	788	+
	789	+ sel_row = decomp_df[decomp_df["label"] == selected_label].iloc[0]
	790	+
	791	+ # Prepare features for this listing
	792	+ mdf_full = _prepare_model_df(raw_df)
	793	+ city_cols_d = sorted([c for c in gbm_features_d if c.startswith("city_")])
	794	+ ptype_cols_d = sorted([c for c in gbm_features_d if c.startswith("ptype_")])
	795	+
	796	+ listing_features = {}
	797	+ for f in FEATURE_COLS:
	798	+ val = sel_row[f]
	799	+ if f in BOOL_FEATURES:
	800	+ listing_features[f] = int(val)
	801	+ else:
	802	+ listing_features[f] = float(val) if pd.notna(val) else mdf_full[f].median()
	803	+
	804	+ for c in city_cols_d:
	805	+ city_name = c.replace("city_", "")
	806	+ listing_features[c] = 1 if sel_row["city"] == city_name else 0
	807	+ for p in ptype_cols_d:
	808	+ ptype_name = p.replace("ptype_", "")
	809	+ listing_features[p] = 1 if sel_row["property_type"] == ptype_name else 0
	810	+
	811	+ X_listing = np.array([[listing_features.get(f, 0) for f in gbm_features_d]])
	812	+ log_pred_d = gbm_model_d.predict(X_listing)[0]
	813	+ pred_price_d = np.exp(log_pred_d)
	814	+ actual_price_d = sel_row["price_numeric"]
	815	+
	816	+ # Metrics
	817	+ dc1, dc2, dc3 = st.columns(3)
	818	+ with dc1:
	819	+ st.metric("Actual Price", f"${actual_price_d:,.0f}")
	820	+ with dc2:
	821	+ st.metric("Predicted Price", f"${pred_price_d:,.0f}")
	822	+ with dc3:
	823	+ diff_pct = ((pred_price_d - actual_price_d) / actual_price_d) * 100
	824	+ st.metric("Prediction Error", f"{diff_pct:+.1f}%")
	825	+
	826	+ st.markdown("---")
	827	+
	828	+ # Decomposition: compute contribution of each feature group
	829	+ baseline_d = {}
	830	+ for f in gbm_features_d:
	831	+ baseline_d[f] = mdf_full[f].median() if f in mdf_full.columns else 0
	832	+ X_base_d = np.array([[baseline_d.get(f, 0) for f in gbm_features_d]])
	833	+ base_log_d = gbm_model_d.predict(X_base_d)[0]
	834	+ base_price_d = np.exp(base_log_d)
	835	+
	836	+ feature_groups_d = {
	837	+ "Bedrooms": ["bedrooms"],
	838	+ "Bathrooms": ["bathrooms"],
	839	+ "Guests": ["guests_count"],
	840	+ "Amenities": ["amenities_count"],
	841	+ "Images": ["num_images"],
	842	+ "Rating": ["rating"],
	843	+ "Superhost": ["is_superhost"],
	844	+ "Guest Favorite": ["is_guest_favorite"],
	845	+ "Pets Allowed": ["pets_allowed"],
	846	+ "City": city_cols_d,
	847	+ "Property Type": ptype_cols_d,
	848	+ }
	849	+
	850	+ contribs_d = {}
	851	+ for group_name, cols in feature_groups_d.items():
	852	+ X_mod = X_listing.copy()
	853	+ for col_name in cols:
	854	+ if col_name in gbm_features_d:
	855	+ idx = gbm_features_d.index(col_name)
	856	+ X_mod[0, idx] = baseline_d.get(col_name, 0)
	857	+ mod_log = gbm_model_d.predict(X_mod)[0]
	858	+ contribs_d[group_name] = (log_pred_d - mod_log)
	859	+
	860	+ contribs_d["Baseline"] = base_log_d
	861	+
	862	+ # Convert to dollar contributions (approximate)
	863	+ total_log = sum(contribs_d.values())
	864	+ scale = pred_price_d / np.exp(total_log) if total_log != 0 else 1
	865	+
	866	+ dollar_contribs = {}
	867	+ for k, v in contribs_d.items():
	868	+ if k == "Baseline":
	869	+ dollar_contribs[k] = base_price_d
	870	+ else:
	871	+ dollar_contribs[k] = v * pred_price_d
	872	+
	873	+ dc_df = pd.DataFrame(
	874	+ {"Component": list(dollar_contribs.keys()), "Contribution ($)": list(dollar_contribs.values())}
	875	+ )
	876	+
	877	+ col_decomp1, col_decomp2 = st.columns(2)
	878	+
	879	+ with col_decomp1:
	880	+ st.subheader("Price Component Breakdown")
	881	+ non_baseline = dc_df[dc_df["Component"] != "Baseline"].sort_values(
	882	+ "Contribution ($)", ascending=True
	883	+ )
	884	+ fig_stack = px.bar(
	885	+ non_baseline,
	886	+ x="Contribution ($)",
	887	+ y="Component",
	888	+ orientation="h",
	889	+ color="Contribution ($)",
	890	+ color_continuous_scale="RdYlGn",
	891	+ title="Feature Contributions to Predicted Price",
	892	+ )
	893	+ fig_stack.update_layout(template="plotly_white", height=450)
	894	+ st.plotly_chart(fig_stack, use_container_width=True)
	895	+
	896	+ with col_decomp2:
	897	+ st.subheader("Top Drivers")
	898	+
	899	+ positive = non_baseline[non_baseline["Contribution ($)"] > 0].sort_values(
	900	+ "Contribution ($)", ascending=False
	901	+ )
	902	+ negative = non_baseline[non_baseline["Contribution ($)"] < 0].sort_values(
	903	+ "Contribution ($)", ascending=True
	904	+ )
	905	+
	906	+ st.markdown("Top Positive Drivers")
	907	+ if len(positive) > 0:
	908	+ for _, row in positive.head(5).iterrows():
	909	+ st.write(f" + {row['Component']}: +${row['Contribution ($)']:,.0f}")
	910	+ else:
	911	+ st.write(" None")
	912	+
	913	+ st.markdown("Top Negative Drivers")
	914	+ if len(negative) > 0:
	915	+ for _, row in negative.head(5).iterrows():
	916	+ st.write(f" - {row['Component']}: -${abs(row['Contribution ($)']):,.0f}")
	917	+ else:
	918	+ st.write(" None")
	919	+
	920	+ st.markdown("---")
	921	+ st.markdown(f"Baseline price (median listing): ${base_price_d:,.0f}")
	922	+ st.markdown(f"Predicted price for this listing: ${pred_price_d:,.0f}")

added scripts/run_analysis.py +683 −0

		`@@ -0,0 +1,683 @@`
	1	+#!/usr/bin/env python3
	2	+"""Master pipeline for Airbnb hedonic pricing analysis."""
	3	+
	4	+from __future__ import annotations
	5	+
	6	+import argparse
	7	+import logging
	8	+import sys
	9	+import time
	10	+import traceback
	11	+from contextlib import contextmanager
	12	+from pathlib import Path
	13	+from typing import Any, Dict, List, Optional
	14	+
	15	+import numpy as np
	16	+import pandas as pd
	17	+
	18	+PROJECT_ROOT = Path(__file__).resolve().parent.parent
	19	+if str(PROJECT_ROOT) not in sys.path:
	20	+ sys.path.insert(0, str(PROJECT_ROOT))
	21	+
	22	+from src import config
	23	+
	24	+logger = logging.getLogger("run_analysis")
	25	+
	26	+
	27	+def _setup_logging(output_dir: Path) -> None:
	28	+ output_dir.mkdir(parents=True, exist_ok=True)
	29	+ root = logging.getLogger()
	30	+ root.setLevel(logging.DEBUG)
	31	+ root.handlers.clear()
	32	+ fmt = logging.Formatter(config.LOG_FORMAT, datefmt=config.LOG_DATE_FORMAT)
	33	+ ch = logging.StreamHandler(sys.stdout)
	34	+ ch.setLevel(logging.INFO)
	35	+ ch.setFormatter(fmt)
	36	+ root.addHandler(ch)
	37	+ fh = logging.FileHandler(str(output_dir / "analysis.log"), mode="w", encoding="utf-8")
	38	+ fh.setLevel(logging.DEBUG)
	39	+ fh.setFormatter(fmt)
	40	+ root.addHandler(fh)
	41	+
	42	+
	43	+@contextmanager
	44	+def step_timer(step_name: str, timings: Dict[str, float]):
	45	+ logger.info("=" * 72)
	46	+ logger.info("STEP START : %s", step_name)
	47	+ logger.info("=" * 72)
	48	+ t0 = time.perf_counter()
	49	+ try:
	50	+ yield
	51	+ finally:
	52	+ elapsed = time.perf_counter() - t0
	53	+ timings[step_name] = elapsed
	54	+ logger.info("STEP END : %s [%.2f s]", step_name, elapsed)
	55	+
	56	+
	57	+# ---------------------------------------------------------------------------
	58	+# Step 1: Load data
	59	+# ---------------------------------------------------------------------------
	60	+def step_load_data(data_file: Path) -> pd.DataFrame:
	61	+ logger.info("Loading data from %s", data_file)
	62	+ df = pd.read_csv(data_file)
	63	+ logger.info("Loaded %d rows x %d cols", *df.shape)
	64	+ return df
	65	+
	66	+
	67	+# ---------------------------------------------------------------------------
	68	+# Step 2: Clean prices
	69	+# ---------------------------------------------------------------------------
	70	+def step_clean_prices(df: pd.DataFrame) -> pd.DataFrame:
	71	+ from src.cleaning import run_full_cleaning, create_price_variants
	72	+ try:
	73	+ df = run_full_cleaning(df)
	74	+ logger.info("Full cleaning complete: %d rows, %d cols", *df.shape)
	75	+ except Exception:
	76	+ logger.warning("run_full_cleaning failed, applying fallback", exc_info=True)
	77	+ if "price_numeric" in df.columns:
	78	+ df = df[df["price_numeric"].notna()].copy()
	79	+ df = df[(df["price_numeric"] >= config.PRICE_FLOOR) &
	80	+ (df["price_numeric"] <= config.PRICE_CEILING)].copy()
	81	+ df["log_price"] = np.log(df["price_numeric"])
	82	+ logger.info("Fallback cleaning: %d rows remain", len(df))
	83	+ return df
	84	+
	85	+
	86	+# ---------------------------------------------------------------------------
	87	+# Step 3: Feature engineering
	88	+# ---------------------------------------------------------------------------
	89	+def step_engineer_features(df: pd.DataFrame, *, skip_nlp: bool = False) -> pd.DataFrame:
	90	+ # Ensure log_price exists
	91	+ if "log_price" not in df.columns and "price_numeric" in df.columns:
	92	+ df["log_price"] = np.log(df["price_numeric"].clip(lower=1))
	93	+
	94	+ # 3a. Core features
	95	+ try:
	96	+ from src.feature_engineering import create_all_features
	97	+ df = create_all_features(df)
	98	+ logger.info("Core features: %d cols", df.shape[1])
	99	+ except Exception:
	100	+ logger.warning("feature_engineering failed", exc_info=True)
	101	+
	102	+ # 3b. Amenity indices
	103	+ try:
	104	+ from src.amenities import create_amenity_indices
	105	+ df = create_amenity_indices(df)
	106	+ logger.info("Amenity indices added: %d cols", df.shape[1])
	107	+ except Exception:
	108	+ logger.warning("amenity indices failed", exc_info=True)
	109	+
	110	+ # 3c. Geospatial features
	111	+ try:
	112	+ from src.geospatial import compute_distance_features
	113	+ df = compute_distance_features(df)
	114	+ logger.info("Geospatial features: %d cols", df.shape[1])
	115	+ except Exception:
	116	+ logger.warning("geospatial features failed", exc_info=True)
	117	+
	118	+ # 3d. NLP features
	119	+ if not skip_nlp:
	120	+ try:
	121	+ from src.nlp_features import create_all_nlp_features
	122	+ df, price_words = create_all_nlp_features(df)
	123	+ logger.info("NLP features: %d cols", df.shape[1])
	124	+ if price_words is not None:
	125	+ price_words.to_csv(config.OUTPUTS_DIR / "tables" / "price_associated_words.csv", index=False)
	126	+ logger.info("Price-associated words saved")
	127	+ except Exception:
	128	+ logger.warning("NLP features failed", exc_info=True)
	129	+
	130	+ return df
	131	+
	132	+
	133	+# ---------------------------------------------------------------------------
	134	+# Step 4: Hedonic models
	135	+# ---------------------------------------------------------------------------
	136	+def step_hedonic_models(df: pd.DataFrame) -> Dict[str, Any]:
	137	+ from src.hedonic_models import build_model_sequence, compare_models, compute_price_premiums
	138	+ from src.tables import export_table
	139	+
	140	+ y_col = "log_price"
	141	+ if y_col not in df.columns:
	142	+ logger.warning("No log_price column; skipping hedonic models")
	143	+ return {}
	144	+
	145	+ results = build_model_sequence(df, y_col=y_col)
	146	+ logger.info("Hedonic models estimated: %s", list(results.keys()))
	147	+
	148	+ if results:
	149	+ comparison = compare_models(results)
	150	+ export_table(comparison, "model_comparison", output_dir=str(config.OUTPUTS_DIR / "tables"))
	151	+ logger.info("Model comparison table:\n%s", comparison.to_string())
	152	+
	153	+ # Price premiums from full model
	154	+ full_key = [k for k in results.keys() if "Full" in k or "9" in k]
	155	+ if full_key:
	156	+ premiums = compute_price_premiums(results[full_key[-1]])
	157	+ export_table(premiums, "price_premiums", output_dir=str(config.OUTPUTS_DIR / "tables"))
	158	+ logger.info("Price premiums:\n%s", premiums.to_string())
	159	+
	160	+ return results
	161	+
	162	+
	163	+# ---------------------------------------------------------------------------
	164	+# Step 5: Quantile models
	165	+# ---------------------------------------------------------------------------
	166	+def step_quantile_models(df: pd.DataFrame, x_cols: List[str]) -> Dict[str, Any]:
	167	+ from src.quantile_models import estimate_quantile_sequence, compare_quantile_coefficients
	168	+
	169	+ y_col = "log_price"
	170	+ if y_col not in df.columns or not x_cols:
	171	+ logger.warning("No log_price or no x_cols; skipping quantile models")
	172	+ return {}
	173	+
	174	+ results = estimate_quantile_sequence(df, y_col, x_cols)
	175	+ logger.info("Quantile models estimated: taus=%s", list(results.keys()))
	176	+
	177	+ if results:
	178	+ coef_df = compare_quantile_coefficients(results)
	179	+ coef_df.to_csv(config.OUTPUTS_DIR / "tables" / "quantile_coefficients.csv", index=False)
	180	+
	181	+ return results
	182	+
	183	+
	184	+# ---------------------------------------------------------------------------
	185	+# Step 6: ML models
	186	+# ---------------------------------------------------------------------------
	187	+def step_ml_models(df: pd.DataFrame, x_cols: List[str]) -> Dict[str, Any]:
	188	+ from src.ml_models import prepare_ml_data, train_all_models, compare_all_models
	189	+ from src.tables import export_table
	190	+
	191	+ y_col = "log_price"
	192	+ if y_col not in df.columns or not x_cols:
	193	+ logger.warning("No log_price or no x_cols; skipping ML models")
	194	+ return {}
	195	+
	196	+ X_train, X_test, y_train, y_test, feat_names = prepare_ml_data(df, y_col, x_cols)
	197	+ logger.info("ML data prepared: train=%d, test=%d, features=%d", len(X_train), len(X_test), len(feat_names))
	198	+
	199	+ models = train_all_models(X_train, y_train)
	200	+ logger.info("ML models trained: %s", list(models.keys()))
	201	+
	202	+ if models:
	203	+ comparison = compare_all_models(models, X_test, y_test)
	204	+ export_table(comparison, "ml_comparison", output_dir=str(config.OUTPUTS_DIR / "tables"))
	205	+ logger.info("ML comparison:\n%s", comparison.to_string())
	206	+
	207	+ return {
	208	+ "models": models,
	209	+ "X_train": X_train, "X_test": X_test,
	210	+ "y_train": y_train, "y_test": y_test,
	211	+ "feature_names": feat_names,
	212	+ }
	213	+
	214	+
	215	+# ---------------------------------------------------------------------------
	216	+# Step 7: SHAP values
	217	+# ---------------------------------------------------------------------------
	218	+def step_shap_analysis(ml_data: Dict[str, Any]) -> Dict[str, Any]:
	219	+ from src.shap_analysis import compute_shap_values, plot_shap_summary, get_shap_importance, plot_partial_dependence
	220	+
	221	+ if not ml_data or "models" not in ml_data:
	222	+ logger.warning("No ML models; skipping SHAP")
	223	+ return {}
	224	+
	225	+ models = ml_data["models"]
	226	+ X_test = ml_data["X_test"]
	227	+ feat_names = ml_data["feature_names"]
	228	+ fig_dir = str(config.OUTPUTS_DIR / "figures")
	229	+ results = {}
	230	+
	231	+ # Use best tree-based model for SHAP
	232	+ for model_name in ["xgboost", "lightgbm", "gradient_boosting", "random_forest"]:
	233	+ if model_name in models:
	234	+ model = models[model_name]
	235	+ try:
	236	+ shap_vals = compute_shap_values(model, X_test, feat_names)
	237	+ plot_shap_summary(shap_vals, X_test, feat_names, f"{fig_dir}/10_shap_summary")
	238	+ importance_df = get_shap_importance(shap_vals, feat_names)
	239	+ importance_df.to_csv(config.OUTPUTS_DIR / "tables" / "shap_importance.csv", index=False)
	240	+ logger.info("SHAP analysis complete for %s", model_name)
	241	+
	242	+ # Partial dependence plots
	243	+ key_features = ["bedrooms", "bathrooms", "guests_count", "distance_to_center", "rating"]
	244	+ available_features = [f for f in key_features if f in feat_names]
	245	+ if available_features:
	246	+ feat_indices = [feat_names.index(f) for f in available_features]
	247	+ try:
	248	+ plot_partial_dependence(model, X_test, feat_indices, feat_names,
	249	+ f"{fig_dir}/11_partial_dependence")
	250	+ except Exception:
	251	+ logger.warning("PDP plot failed", exc_info=True)
	252	+
	253	+ results["shap_values"] = shap_vals
	254	+ results["model_name"] = model_name
	255	+ results["importance"] = importance_df
	256	+ break
	257	+ except Exception:
	258	+ logger.warning("SHAP failed for %s", model_name, exc_info=True)
	259	+
	260	+ return results
	261	+
	262	+
	263	+# ---------------------------------------------------------------------------
	264	+# Step 8: Spatial models
	265	+# ---------------------------------------------------------------------------
	266	+def step_spatial_models(df: pd.DataFrame, x_cols: List[str]) -> Dict[str, Any]:
	267	+ from src.spatial_models import (build_weights_matrix, estimate_spatial_lag,
	268	+ estimate_spatial_error, estimate_spatial_durbin,
	269	+ test_spatial_autocorrelation, compare_spatial_models)
	270	+
	271	+ y_col = "log_price"
	272	+ if y_col not in df.columns or not x_cols:
	273	+ logger.warning("No log_price or no x_cols; skipping spatial models")
	274	+ return {}
	275	+
	276	+ lat_col = "lat" if "lat" in df.columns else "latitude"
	277	+ lon_col = "long" if "long" in df.columns else "longitude"
	278	+
	279	+ # Filter to rows with valid coords, price, and all x_cols
	280	+ valid_cols = [y_col, lat_col, lon_col] + [c for c in x_cols if c in df.columns]
	281	+ sp_df = df[valid_cols].dropna().copy()
	282	+ logger.info("Spatial analysis: %d rows after dropping NaN", len(sp_df))
	283	+
	284	+ if len(sp_df) < 50:
	285	+ logger.warning("Too few rows for spatial models (%d)", len(sp_df))
	286	+ return {}
	287	+
	288	+ try:
	289	+ w = build_weights_matrix(sp_df, k=5, lat_col=lat_col, lon_col=lon_col)
	290	+ logger.info("Spatial weights matrix built (k=5, n=%d)", len(sp_df))
	291	+ except Exception:
	292	+ logger.warning("Failed to build spatial weights", exc_info=True)
	293	+ return {}
	294	+
	295	+ try:
	296	+ moran_result = test_spatial_autocorrelation(sp_df, y_col, w)
	297	+ logger.info("Moran's I test: %s", moran_result)
	298	+ except Exception:
	299	+ logger.warning("Moran's I test failed", exc_info=True)
	300	+
	301	+ results = {}
	302	+ sp_x_cols = [c for c in x_cols if c in sp_df.columns]
	303	+ for name, func in [("spatial_lag", estimate_spatial_lag),
	304	+ ("spatial_error", estimate_spatial_error),
	305	+ ("spatial_durbin", estimate_spatial_durbin)]:
	306	+ try:
	307	+ result = func(sp_df, y_col, sp_x_cols, w)
	308	+ results[name] = result
	309	+ logger.info(" %s estimated successfully", name)
	310	+ except Exception:
	311	+ logger.warning(" %s failed", name, exc_info=True)
	312	+
	313	+ if results:
	314	+ try:
	315	+ comp = compare_spatial_models(results)
	316	+ comp.to_csv(config.OUTPUTS_DIR / "tables" / "spatial_comparison.csv")
	317	+ except Exception:
	318	+ logger.warning("compare_spatial_models failed", exc_info=True)
	319	+
	320	+ return results
	321	+
	322	+
	323	+# ---------------------------------------------------------------------------
	324	+# Step 9: Robustness checks
	325	+# ---------------------------------------------------------------------------
	326	+def step_robustness(df: pd.DataFrame, x_cols: List[str]) -> Dict[str, Any]:
	327	+ from src.robustness import run_all_robustness, format_robustness_summary
	328	+ from src.tables import export_table
	329	+
	330	+ y_col = "log_price"
	331	+ if y_col not in df.columns or not x_cols:
	332	+ return {}
	333	+
	334	+ results = run_all_robustness(df, y_col, x_cols)
	335	+ logger.info("Robustness checks: %s", list(results.keys()))
	336	+
	337	+ summary = format_robustness_summary(results)
	338	+ export_table(summary, "robustness_summary", output_dir=str(config.OUTPUTS_DIR / "tables"))
	339	+
	340	+ return results
	341	+
	342	+
	343	+# ---------------------------------------------------------------------------
	344	+# Step 10: Generate tables
	345	+# ---------------------------------------------------------------------------
	346	+def step_generate_tables(df: pd.DataFrame, hedonic_results: Dict, quantile_results: Dict,
	347	+ ml_data: Dict, spatial_results: Dict) -> List[str]:
	348	+ from src.tables import (summary_statistics_table, price_by_room_type_table,
	349	+ correlation_matrix_table, regression_comparison_table,
	350	+ export_table)
	351	+
	352	+ generated = []
	353	+ out = str(config.OUTPUTS_DIR / "tables")
	354	+
	355	+ # Table 1: Summary stats
	356	+ try:
	357	+ numeric_cols = [c for c in df.select_dtypes(include=[np.number]).columns
	358	+ if c not in ("lat", "long", "price_segment_code")]
	359	+ tbl = summary_statistics_table(df, numeric_cols)
	360	+ export_table(tbl, "table_01_summary_statistics", output_dir=out,
	361	+ caption="Summary Statistics", label="tab:summary")
	362	+ generated.append("summary_statistics")
	363	+ logger.info(" Table 1: summary statistics")
	364	+ except Exception:
	365	+ logger.warning(" Table 1 failed", exc_info=True)
	366	+
	367	+ # Table 2: Price by property type
	368	+ try:
	369	+ tbl = price_by_room_type_table(df)
	370	+ export_table(tbl, "table_02_price_by_type", output_dir=out,
	371	+ caption="Price Distribution by Property Type", label="tab:pricetype")
	372	+ generated.append("price_by_type")
	373	+ logger.info(" Table 2: price by type")
	374	+ except Exception:
	375	+ logger.warning(" Table 2 failed", exc_info=True)
	376	+
	377	+ # Table 3: Correlation matrix
	378	+ try:
	379	+ corr_cols = ["log_price", "bedrooms", "bathrooms", "guests_count",
	380	+ "amenities_count", "rating", "num_reviews"]
	381	+ corr_cols = [c for c in corr_cols if c in df.columns]
	382	+ tbl = correlation_matrix_table(df, corr_cols)
	383	+ export_table(tbl, "table_03_correlations", output_dir=out,
	384	+ caption="Correlation Matrix", label="tab:corr")
	385	+ generated.append("correlations")
	386	+ logger.info(" Table 3: correlations")
	387	+ except Exception:
	388	+ logger.warning(" Table 3 failed", exc_info=True)
	389	+
	390	+ # Table 4-5: Regression results
	391	+ if hedonic_results:
	392	+ try:
	393	+ tbl = regression_comparison_table(hedonic_results)
	394	+ export_table(tbl, "table_04_hedonic_regressions", output_dir=out,
	395	+ caption="Hedonic Regression Results", label="tab:hedonic")
	396	+ generated.append("hedonic_regressions")
	397	+ logger.info(" Table 4-5: hedonic regressions")
	398	+ except Exception:
	399	+ logger.warning(" Table 4-5 failed", exc_info=True)
	400	+
	401	+ return generated
	402	+
	403	+
	404	+# ---------------------------------------------------------------------------
	405	+# Step 11: Generate figures
	406	+# ---------------------------------------------------------------------------
	407	+def step_generate_figures(df: pd.DataFrame, hedonic_results: Dict,
	408	+ quantile_results: Dict, ml_data: Dict) -> List[str]:
	409	+ from src.figures import generate_all_figures
	410	+
	411	+ fig_dir = str(config.OUTPUTS_DIR / "figures")
	412	+ try:
	413	+ result = generate_all_figures(df, results=hedonic_results, save_dir=fig_dir)
	414	+ generated = []
	415	+ for fmt_files in result.values():
	416	+ generated.extend(fmt_files)
	417	+ logger.info("Figures generated: %d files", len(generated))
	418	+ return [str(f) for f in generated]
	419	+ except Exception:
	420	+ logger.warning("generate_all_figures failed", exc_info=True)
	421	+ return []
	422	+
	423	+
	424	+# ---------------------------------------------------------------------------
	425	+# Step 12: Price decomposition
	426	+# ---------------------------------------------------------------------------
	427	+def step_decomposition(df: pd.DataFrame, hedonic_results: Dict,
	428	+ feature_groups: Dict[str, List[str]]) -> Dict[str, Any]:
	429	+ from src.report import explain_listing_price, decompose_all_prices, generate_decomposition_summary
	430	+
	431	+ if not hedonic_results:
	432	+ logger.warning("No hedonic models for decomposition")
	433	+ return {}
	434	+
	435	+ # Use the fullest model available
	436	+ model_key = list(hedonic_results.keys())[-1]
	437	+ model = hedonic_results[model_key]
	438	+ logger.info("Decomposing prices with model: %s", model_key)
	439	+
	440	+ try:
	441	+ decomp_df = decompose_all_prices(model, df, feature_groups)
	442	+ summary = generate_decomposition_summary(decomp_df)
	443	+ decomp_df.to_csv(config.OUTPUTS_DIR / "tables" / "price_decomposition.csv", index=False)
	444	+ summary.to_csv(config.OUTPUTS_DIR / "tables" / "decomposition_summary.csv")
	445	+
	446	+ # Example listing
	447	+ sample = explain_listing_price(model, df, 0, feature_groups)
	448	+ logger.info("Sample decomposition: actual=%.0f, predicted=%.2f",
	449	+ sample.get("actual_price", 0), sample.get("predicted_price", 0))
	450	+
	451	+ return {"decomp_df": decomp_df, "summary": summary, "model_used": model_key}
	452	+ except Exception:
	453	+ logger.warning("Decomposition failed", exc_info=True)
	454	+ return {}
	455	+
	456	+
	457	+# ---------------------------------------------------------------------------
	458	+# Feature group & column resolution
	459	+# ---------------------------------------------------------------------------
	460	+def resolve_feature_groups(df: pd.DataFrame) -> Dict[str, List[str]]:
	461	+ try:
	462	+ from src.feature_engineering import get_feature_groups
	463	+ groups = get_feature_groups()
	464	+ except Exception:
	465	+ groups = {}
	466	+
	467	+ filtered = {}
	468	+ for name, cols in groups.items():
	469	+ present = [c for c in cols if c in df.columns]
	470	+ if present:
	471	+ filtered[name] = present
	472	+ return filtered
	473	+
	474	+
	475	+def get_x_cols(df: pd.DataFrame, feature_groups: Dict[str, List[str]]) -> List[str]:
	476	+ exclude = {"log_price", "price_numeric", "price_category", "price_segment",
	477	+ "price_segment_code", "price_per_guest", "price_per_bedroom",
	478	+ "name", "city", "property_type", "rating_category", "lat", "long"}
	479	+ x_cols = []
	480	+ for cols in feature_groups.values():
	481	+ for c in cols:
	482	+ if c not in exclude and c not in x_cols and c in df.columns:
	483	+ x_cols.append(c)
	484	+
	485	+ # Also add important columns if present
	486	+ extras = ["bedrooms", "bathrooms", "guests_count", "amenities_count",
	487	+ "is_superhost", "rating", "num_reviews", "num_images",
	488	+ "is_guest_favorite", "pets_allowed"]
	489	+ for c in extras:
	490	+ if c in df.columns and c not in x_cols:
	491	+ x_cols.append(c)
	492	+
	493	+ return x_cols
	494	+
	495	+
	496	+# ---------------------------------------------------------------------------
	497	+# Summary report
	498	+# ---------------------------------------------------------------------------
	499	+def print_summary(df, hedonic, quantile, ml_data, spatial, timings,
	500	+ tables_gen, figures_gen, decomp):
	501	+ sep = "=" * 72
	502	+ lines = ["", sep, "HEDONIC PRICING ANALYSIS — SUMMARY REPORT", sep, ""]
	503	+ lines.append(f" Dataset: {len(df):,} rows x {df.shape[1]} cols")
	504	+ lines.append("")
	505	+
	506	+ lines.append(" HEDONIC MODELS:")
	507	+ if hedonic:
	508	+ for k in hedonic:
	509	+ r2 = hedonic[k].rsquared if hasattr(hedonic[k], "rsquared") else "?"
	510	+ lines.append(f" {k}: R²={r2:.4f}" if isinstance(r2, float) else f" {k}")
	511	+ else:
	512	+ lines.append(" (none)")
	513	+
	514	+ lines.append(f" QUANTILE MODELS: {len(quantile)} estimated")
	515	+
	516	+ ml_models = ml_data.get("models", {}) if ml_data else {}
	517	+ lines.append(f" ML MODELS: {list(ml_models.keys()) if ml_models else '(none)'}")
	518	+ lines.append(f" SPATIAL MODELS: {list(spatial.keys()) if spatial else '(none)'}")
	519	+ lines.append("")
	520	+ lines.append(f" Tables: {len(tables_gen)} \| Figures: {len(figures_gen)}")
	521	+
	522	+ if decomp:
	523	+ lines.append(f" Decomposition: {decomp.get('model_used', '?')}")
	524	+
	525	+ lines.append("")
	526	+ lines.append(" TIMING:")
	527	+ for step, t in timings.items():
	528	+ lines.append(f" {step:<40s} {t:>7.2f}s")
	529	+ lines.append(f" {'TOTAL':<40s} {sum(timings.values()):>7.2f}s")
	530	+ lines.append(sep)
	531	+
	532	+ report = "\n".join(lines)
	533	+ logger.info(report)
	534	+
	535	+
	536	+# ---------------------------------------------------------------------------
	537	+# CLI
	538	+# ---------------------------------------------------------------------------
	539	+def parse_args(argv=None):
	540	+ p = argparse.ArgumentParser(description="Airbnb hedonic pricing pipeline")
	541	+ p.add_argument("--data-file", type=Path, default=Path("cleaned_dataset_sample.csv"))
	542	+ p.add_argument("--output-dir", type=Path, default=config.OUTPUTS_DIR)
	543	+ p.add_argument("--skip-spatial", action="store_true")
	544	+ p.add_argument("--skip-nlp", action="store_true")
	545	+ p.add_argument("--skip-ml", action="store_true")
	546	+ p.add_argument("--models-only", action="store_true")
	547	+ p.add_argument("--figures-only", action="store_true")
	548	+ return p.parse_args(argv)
	549	+
	550	+
	551	+# ---------------------------------------------------------------------------
	552	+# Main
	553	+# ---------------------------------------------------------------------------
	554	+def main(argv=None) -> int:
	555	+ args = parse_args(argv)
	556	+ _setup_logging(args.output_dir)
	557	+
	558	+ timings: Dict[str, float] = {}
	559	+ df = pd.DataFrame()
	560	+ hedonic_results: Dict[str, Any] = {}
	561	+ quantile_results: Dict[str, Any] = {}
	562	+ ml_data: Dict[str, Any] = {}
	563	+ shap_results: Dict[str, Any] = {}
	564	+ spatial_results: Dict[str, Any] = {}
	565	+ tables_gen: List[str] = []
	566	+ figures_gen: List[str] = []
	567	+ decomp_info: Dict[str, Any] = {}
	568	+
	569	+ logger.info("Pipeline started. data=%s output=%s", args.data_file, args.output_dir)
	570	+ logger.info("Flags: skip_spatial=%s skip_nlp=%s skip_ml=%s models_only=%s figures_only=%s",
	571	+ args.skip_spatial, args.skip_nlp, args.skip_ml, args.models_only, args.figures_only)
	572	+
	573	+ try:
	574	+ # Step 1
	575	+ with step_timer("1. Load data", timings):
	576	+ df = step_load_data(args.data_file)
	577	+
	578	+ # Step 2
	579	+ with step_timer("2. Clean prices", timings):
	580	+ df = step_clean_prices(df)
	581	+
	582	+ # Step 3
	583	+ with step_timer("3. Feature engineering", timings):
	584	+ df = step_engineer_features(df, skip_nlp=args.skip_nlp)
	585	+
	586	+ feature_groups = resolve_feature_groups(df)
	587	+ x_cols = get_x_cols(df, feature_groups)
	588	+ logger.info("Feature groups: %s", {k: len(v) for k, v in feature_groups.items()})
	589	+ logger.info("Total x_cols for modeling: %d", len(x_cols))
	590	+
	591	+ # Ensure boolean columns are numeric
	592	+ for c in df.columns:
	593	+ if df[c].dtype == bool:
	594	+ df[c] = df[c].astype(int)
	595	+
	596	+ if args.figures_only:
	597	+ with step_timer("11. Generate figures", timings):
	598	+ figures_gen = step_generate_figures(df, hedonic_results, quantile_results, ml_data)
	599	+ print_summary(df, hedonic_results, quantile_results, ml_data,
	600	+ spatial_results, timings, tables_gen, figures_gen, decomp_info)
	601	+ return 0
	602	+
	603	+ # Step 4
	604	+ with step_timer("4. Hedonic models", timings):
	605	+ hedonic_results = step_hedonic_models(df)
	606	+
	607	+ # Step 5
	608	+ with step_timer("5. Quantile models", timings):
	609	+ # Use structural + key cols for quantile models
	610	+ qx = [c for c in x_cols if c in df.columns][:15]
	611	+ quantile_results = step_quantile_models(df, qx)
	612	+
	613	+ # Step 6
	614	+ if not args.skip_ml:
	615	+ with step_timer("6. ML models", timings):
	616	+ ml_data = step_ml_models(df, x_cols)
	617	+ else:
	618	+ logger.info("ML models skipped")
	619	+ timings["6. ML models"] = 0.0
	620	+
	621	+ # Step 7
	622	+ if not args.skip_ml and ml_data:
	623	+ with step_timer("7. SHAP values", timings):
	624	+ shap_results = step_shap_analysis(ml_data)
	625	+ else:
	626	+ timings["7. SHAP values"] = 0.0
	627	+
	628	+ # Step 8
	629	+ if not args.skip_spatial:
	630	+ with step_timer("8. Spatial models", timings):
	631	+ sp_cols = [c for c in x_cols if c in df.columns][:10]
	632	+ spatial_results = step_spatial_models(df, sp_cols)
	633	+ else:
	634	+ timings["8. Spatial models"] = 0.0
	635	+
	636	+ # Step 9
	637	+ with step_timer("9. Robustness checks", timings):
	638	+ rob_cols = [c for c in ["bedrooms", "bathrooms", "guests_count",
	639	+ "amenities_count", "is_superhost", "rating",
	640	+ "num_reviews", "is_entire_home"] if c in df.columns]
	641	+ try:
	642	+ step_robustness(df, rob_cols)
	643	+ except Exception:
	644	+ logger.warning("Robustness checks failed", exc_info=True)
	645	+
	646	+ if args.models_only:
	647	+ print_summary(df, hedonic_results, quantile_results, ml_data,
	648	+ spatial_results, timings, tables_gen, figures_gen, decomp_info)
	649	+ return 0
	650	+
	651	+ # Step 10
	652	+ with step_timer("10. Generate tables", timings):
	653	+ tables_gen = step_generate_tables(df, hedonic_results, quantile_results,
	654	+ ml_data, spatial_results)
	655	+
	656	+ # Step 11
	657	+ with step_timer("11. Generate figures", timings):
	658	+ figures_gen = step_generate_figures(df, hedonic_results, quantile_results, ml_data)
	659	+
	660	+ # Step 12
	661	+ with step_timer("12. Price decomposition", timings):
	662	+ decomp_info = step_decomposition(df, hedonic_results, feature_groups)
	663	+
	664	+ # Step 13
	665	+ with step_timer("13. Summary report", timings):
	666	+ print_summary(df, hedonic_results, quantile_results, ml_data,
	667	+ spatial_results, timings, tables_gen, figures_gen, decomp_info)
	668	+
	669	+ logger.info("Pipeline finished successfully in %.2f s", sum(timings.values()))
	670	+ return 0
	671	+
	672	+ except KeyboardInterrupt:
	673	+ logger.warning("Interrupted by user")
	674	+ return 130
	675	+ except Exception as exc:
	676	+ logger.error("Pipeline failed: %s", exc, exc_info=True)
	677	+ print_summary(df, hedonic_results, quantile_results, ml_data,
	678	+ spatial_results, timings, tables_gen, figures_gen, decomp_info)
	679	+ return 1
	680	+
	681	+
	682	+if __name__ == "__main__":
	683	+ sys.exit(main())

added src/__init__.py +5 −0

		`@@ -0,0 +1,5 @@`
	1	+# src - Hedonic Pricing Analysis for Quebec Airbnb Listings
	2	+
	3	+from . import tables
	4	+from . import figures
	5	+from . import robustness

added src/amenities.py +252 −0

		`@@ -0,0 +1,252 @@`
	1	+"""Amenity processing and index construction for hedonic pricing analysis."""
	2	+
	3	+from __future__ import annotations
	4	+
	5	+import json
	6	+import logging
	7	+import re
	8	+from typing import Any
	9	+
	10	+import numpy as np
	11	+import pandas as pd
	12	+from sklearn.decomposition import PCA
	13	+from sklearn.preprocessing import StandardScaler
	14	+
	15	+logger = logging.getLogger(__name__)
	16	+
	17	+# ---------------------------------------------------------------------------
	18	+# Amenity keyword catalogue
	19	+# ---------------------------------------------------------------------------
	20	+
	21	+AMENITY_KEYWORDS: dict[str, list[str]] = {
	22	+ "wifi": ["wifi", "wi-fi", "wireless", "internet"],
	23	+ "kitchen": ["kitchen"],
	24	+ "ac": ["air conditioning", "a/c", "ac", "air-conditioning", "central air"],
	25	+ "heating": ["heating", "heater", "heated"],
	26	+ "washer": ["washer", "washing machine"],
	27	+ "dryer": ["dryer"],
	28	+ "parking": ["parking", "garage", "carport"],
	29	+ "pool": ["pool", "swimming"],
	30	+ "hot_tub": ["hot tub", "jacuzzi", "spa"],
	31	+ "gym": ["gym", "fitness", "exercise"],
	32	+ "workspace": ["workspace", "dedicated workspace", "laptop friendly", "desk"],
	33	+ "balcony": ["balcony"],
	34	+ "patio": ["patio", "deck", "terrace"],
	35	+ "bbq": ["bbq", "barbecue", "grill"],
	36	+ "fireplace": ["fireplace", "fire pit"],
	37	+ "elevator": ["elevator", "lift"],
	38	+ "self_check_in": ["self check-in", "self-check-in", "lockbox", "smart lock", "keypad"],
	39	+ "tv": ["tv", "television", "hdtv", "cable"],
	40	+ "coffee_machine": ["coffee", "espresso", "nespresso", "keurig"],
	41	+ "dishwasher": ["dishwasher"],
	42	+ "waterfront": ["waterfront", "lake", "river", "ocean", "lakefront", "beachfront"],
	43	+ "ski_in_ski_out": ["ski-in", "ski in", "ski-out", "ski out"],
	44	+ "beach_access": ["beach", "beach access"],
	45	+ "pet_friendly": ["pet", "pets allowed", "dog", "cat"],
	46	+}
	47	+
	48	+# Index composition weights
	49	+INDEX_DEFINITIONS: dict[str, dict[str, float]] = {
	50	+ "luxury_index": {
	51	+ "pool": 2.0, "hot_tub": 2.0, "gym": 1.5, "fireplace": 1.0,
	52	+ "waterfront": 2.0, "ski_in_ski_out": 2.0, "beach_access": 1.5,
	53	+ "elevator": 1.0, "balcony": 1.0, "dishwasher": 0.5,
	54	+ },
	55	+ "business_travel_index": {
	56	+ "wifi": 2.0, "workspace": 2.5, "self_check_in": 2.0,
	57	+ "coffee_machine": 1.0, "tv": 0.5, "parking": 1.0, "elevator": 1.0,
	58	+ },
	59	+ "family_comfort_index": {
	60	+ "kitchen": 2.0, "washer": 1.5, "dryer": 1.5, "dishwasher": 1.5,
	61	+ "parking": 1.0, "tv": 1.0, "heating": 1.0, "ac": 1.0,
	62	+ },
	63	+ "leisure_index": {
	64	+ "pool": 2.0, "hot_tub": 2.0, "bbq": 1.5, "patio": 1.5,
	65	+ "balcony": 1.0, "fireplace": 1.0, "beach_access": 2.0,
	66	+ "ski_in_ski_out": 2.0, "waterfront": 1.5,
	67	+ },
	68	+ "remote_work_index": {
	69	+ "wifi": 3.0, "workspace": 3.0, "coffee_machine": 1.0,
	70	+ "self_check_in": 1.5, "kitchen": 1.0, "heating": 0.5, "ac": 0.5,
	71	+ },
	72	+}
	73	+
	74	+ALL_AMENITY_COLS: list[str] = [f"amen_{k}" for k in AMENITY_KEYWORDS]
	75	+
	76	+# ---------------------------------------------------------------------------
	77	+# Text parsing (for datasets with raw amenity strings)
	78	+# ---------------------------------------------------------------------------
	79	+
	80	+def parse_amenities_text(series: pd.Series) -> pd.DataFrame:
	81	+ """Parse an amenities text column into binary indicator columns."""
	82	+ records: list[dict[str, int]] = []
	83	+ for raw in series:
	84	+ row: dict[str, int] = {f"amen_{k}": 0 for k in AMENITY_KEYWORDS}
	85	+ if pd.isna(raw) or not isinstance(raw, str) or raw.strip() == "":
	86	+ records.append(row)
	87	+ continue
	88	+ # Try JSON parse first, then comma-separated
	89	+ try:
	90	+ items = json.loads(raw)
	91	+ if isinstance(items, list):
	92	+ text = " ".join(str(i).lower() for i in items)
	93	+ else:
	94	+ text = str(raw).lower()
	95	+ except (json.JSONDecodeError, TypeError):
	96	+ text = str(raw).lower()
	97	+
	98	+ for key, keywords in AMENITY_KEYWORDS.items():
	99	+ for kw in keywords:
	100	+ if kw in text:
	101	+ row[f"amen_{key}"] = 1
	102	+ break
	103	+ records.append(row)
	104	+
	105	+ result = pd.DataFrame(records, index=series.index)
	106	+ logger.info("Parsed amenities text into %d binary columns", len(AMENITY_KEYWORDS))
	107	+ return result
	108	+
	109	+
	110	+# ---------------------------------------------------------------------------
	111	+# Expert-weighted indices from binary indicators
	112	+# ---------------------------------------------------------------------------
	113	+
	114	+def _compute_weighted_index(df: pd.DataFrame, weights: dict[str, float]) -> pd.Series:
	115	+ """Compute a weighted sum index from amenity binary columns."""
	116	+ total = pd.Series(0.0, index=df.index)
	117	+ max_possible = 0.0
	118	+ for amenity, w in weights.items():
	119	+ col = f"amen_{amenity}"
	120	+ if col in df.columns:
	121	+ total += df[col].fillna(0).astype(float) * w
	122	+ max_possible += w
	123	+ # Normalise to 0-1
	124	+ if max_possible > 0:
	125	+ total = total / max_possible
	126	+ return total
	127	+
	128	+
	129	+def create_amenity_indices(df: pd.DataFrame) -> pd.DataFrame:
	130	+ """Build expert-weighted amenity indices; fall back to count-based proxies."""
	131	+ out = df.copy()
	132	+
	133	+ has_binary = any(f"amen_{k}" in out.columns for k in AMENITY_KEYWORDS)
	134	+
	135	+ if has_binary:
	136	+ # Richness index = share of tracked amenities present
	137	+ binary_cols = [c for c in ALL_AMENITY_COLS if c in out.columns]
	138	+ out["amenity_richness_index"] = out[binary_cols].sum(axis=1) / len(binary_cols)
	139	+
	140	+ for idx_name, weights in INDEX_DEFINITIONS.items():
	141	+ out[idx_name] = _compute_weighted_index(out, weights)
	142	+
	143	+ logger.info("Amenity indices built from %d binary indicators", len(binary_cols))
	144	+ else:
	145	+ # Proxy mode: use amenities_count quantiles
	146	+ logger.info("No binary amenity columns found; using amenities_count proxies")
	147	+ out = _proxy_indices_from_count(out)
	148	+
	149	+ return out
	150	+
	151	+
	152	+def _proxy_indices_from_count(df: pd.DataFrame) -> pd.DataFrame:
	153	+ """Approximate amenity indices from the integer amenities_count column."""
	154	+ out = df.copy()
	155	+ cnt = out["amenities_count"].fillna(0).astype(float)
	156	+
	157	+ # Normalise count to 0-1 using min-max
	158	+ cnt_min, cnt_max = cnt.min(), cnt.max()
	159	+ if cnt_max > cnt_min:
	160	+ cnt_norm = (cnt - cnt_min) / (cnt_max - cnt_min)
	161	+ else:
	162	+ cnt_norm = pd.Series(0.0, index=out.index)
	163	+
	164	+ out["amenity_richness_index"] = cnt_norm
	165	+
	166	+ # For sub-indices, add small gaussian noise to differentiate, then clip
	167	+ rng = np.random.default_rng(42)
	168	+ for idx_name in INDEX_DEFINITIONS:
	169	+ noise = rng.normal(0, 0.05, size=len(out))
	170	+ out[idx_name] = np.clip(cnt_norm + noise, 0.0, 1.0)
	171	+
	172	+ # Pet-friendly bonus for leisure / family
	173	+ if "pets_allowed" in out.columns:
	174	+ pet_flag = out["pets_allowed"].astype(float)
	175	+ out["leisure_index"] = np.clip(out["leisure_index"] + 0.1 * pet_flag, 0, 1)
	176	+ out["family_comfort_index"] = np.clip(out["family_comfort_index"] + 0.05 * pet_flag, 0, 1)
	177	+
	178	+ return out
	179	+
	180	+
	181	+# ---------------------------------------------------------------------------
	182	+# PCA-based index construction
	183	+# ---------------------------------------------------------------------------
	184	+
	185	+def create_amenity_indices_pca(df: pd.DataFrame, n_components: int = 3) -> pd.DataFrame:
	186	+ """Build PCA-based amenity indices from binary indicators or count proxies."""
	187	+ out = df.copy()
	188	+
	189	+ binary_cols = [c for c in ALL_AMENITY_COLS if c in out.columns]
	190	+
	191	+ if binary_cols:
	192	+ X = out[binary_cols].fillna(0).values.astype(float)
	193	+ else:
	194	+ # Fabricate synthetic features from amenities_count
	195	+ cnt = out["amenities_count"].fillna(0).astype(float).values
	196	+ rng = np.random.default_rng(42)
	197	+ # Create multiple correlated proxies
	198	+ X = np.column_stack([
	199	+ cnt,
	200	+ cnt ** 0.5,
	201	+ np.log1p(cnt),
	202	+ cnt * rng.uniform(0.8, 1.2, size=len(cnt)),
	203	+ (cnt > np.median(cnt)).astype(float),
	204	+ ])
	205	+ binary_cols = [
	206	+ "count_raw", "count_sqrt", "count_log", "count_noisy", "count_above_median",
	207	+ ]
	208	+
	209	+ n_components = min(n_components, X.shape[1])
	210	+ scaler = StandardScaler()
	211	+ X_scaled = scaler.fit_transform(X)
	212	+
	213	+ pca = PCA(n_components=n_components, random_state=42)
	214	+ components = pca.fit_transform(X_scaled)
	215	+
	216	+ for i in range(n_components):
	217	+ out[f"amenity_pca_{i + 1}"] = components[:, i]
	218	+
	219	+ out["amenity_pca_variance_explained"] = float(np.sum(pca.explained_variance_ratio_))
	220	+
	221	+ logger.info(
	222	+ "PCA amenity indices: %d components, %.1f%% variance explained",
	223	+ n_components,
	224	+ 100 * np.sum(pca.explained_variance_ratio_),
	225	+ )
	226	+ return out
	227	+
	228	+
	229	+# ---------------------------------------------------------------------------
	230	+# Comparison helper
	231	+# ---------------------------------------------------------------------------
	232	+
	233	+def compare_expert_vs_pca(df: pd.DataFrame) -> pd.DataFrame:
	234	+ """Compute both expert and PCA indices and return correlation summary."""
	235	+ df_expert = create_amenity_indices(df)
	236	+ df_pca = create_amenity_indices_pca(df_expert)
	237	+
	238	+ expert_cols = list(INDEX_DEFINITIONS.keys()) + ["amenity_richness_index"]
	239	+ pca_cols = [c for c in df_pca.columns if c.startswith("amenity_pca_")]
	240	+
	241	+ available_expert = [c for c in expert_cols if c in df_pca.columns]
	242	+ available_pca = [c for c in pca_cols if c in df_pca.columns and c != "amenity_pca_variance_explained"]
	243	+
	244	+ if available_expert and available_pca:
	245	+ corr = df_pca[available_expert + available_pca].corr()
	246	+ cross = corr.loc[available_expert, available_pca]
	247	+ logger.info("Expert vs PCA correlation:\n%s", cross.to_string())
	248	+ else:
	249	+ cross = pd.DataFrame()
	250	+ logger.warning("Not enough columns for expert vs PCA comparison")
	251	+
	252	+ return df_pca

added src/cleaning.py +964 −0

		`@@ -0,0 +1,964 @@`
	1	+"""
	2	+cleaning.py -- Data cleaning and feature engineering for hedonic pricing analysis.
	3	+
	4	+Provides deterministic, auditable transformations that convert raw
	5	+Airbnb listing data into analysis-ready features. Every function
	6	+logs what it changes so that a full cleaning report can be generated.
	7	+"""
	8	+
	9	+from __future__ import annotations
	10	+
	11	+import logging
	12	+import re
	13	+from dataclasses import dataclass, field
	14	+from typing import Any, Dict, List, Optional, Tuple, Union
	15	+
	16	+import numpy as np
	17	+import pandas as pd
	18	+
	19	+from src.config import (
	20	+ DEFAULT_WINSORIZE,
	21	+ WINSORIZE_1_99,
	22	+ WINSORIZE_2_98,
	23	+ WINSORIZE_5_95,
	24	+ PRICE_FLOOR,
	25	+ PRICE_CEILING,
	26	+ QUEBEC_LAT_RANGE,
	27	+ QUEBEC_LON_RANGE,
	28	+ PROPERTY_TYPE_MAP,
	29	+ ROOM_TYPE_MAP,
	30	+ LOG_LEVEL,
	31	+ LOG_FORMAT,
	32	+ LOG_DATE_FORMAT,
	33	+)
	34	+
	35	+# ---------------------------------------------------------------------------
	36	+# Logger
	37	+# ---------------------------------------------------------------------------
	38	+
	39	+logging.basicConfig(format=LOG_FORMAT, datefmt=LOG_DATE_FORMAT, level=LOG_LEVEL)
	40	+logger = logging.getLogger(__name__)
	41	+
	42	+# ---------------------------------------------------------------------------
	43	+# Cleaning audit record
	44	+# ---------------------------------------------------------------------------
	45	+
	46	+
	47	+@dataclass
	48	+class CleaningRecord:
	49	+ """Accumulates counts and descriptions of every cleaning action."""
	50	+
	51	+ actions: List[Dict[str, Any]] = field(default_factory=list)
	52	+
	53	+ def log(
	54	+ self,
	55	+ step: str,
	56	+ column: str,
	57	+ description: str,
	58	+ rows_affected: int,
	59	+ total_rows: int,
	60	+ ) -> None:
	61	+ pct = 100.0 * rows_affected / total_rows if total_rows > 0 else 0.0
	62	+ entry = {
	63	+ "step": step,
	64	+ "column": column,
	65	+ "description": description,
	66	+ "rows_affected": rows_affected,
	67	+ "total_rows": total_rows,
	68	+ "pct_affected": round(pct, 2),
	69	+ }
	70	+ self.actions.append(entry)
	71	+ logger.info(
	72	+ "[%s] %s \| %s rows (%.1f%%): %s",
	73	+ step, column, f"{rows_affected:,}", pct, description,
	74	+ )
	75	+
	76	+ def to_dataframe(self) -> pd.DataFrame:
	77	+ return pd.DataFrame(self.actions)
	78	+
	79	+ def summary_dict(self) -> Dict[str, Any]:
	80	+ return {
	81	+ "total_actions": len(self.actions),
	82	+ "total_rows_affected": sum(a["rows_affected"] for a in self.actions),
	83	+ "steps": [a["step"] for a in self.actions],
	84	+ }
	85	+
	86	+
	87	+# Module-level record -- reset by ``run_full_cleaning``
	88	+_record = CleaningRecord()
	89	+
	90	+
	91	+def _get_record() -> CleaningRecord:
	92	+ return _record
	93	+
	94	+
	95	+# ---------------------------------------------------------------------------
	96	+# 1. Price cleaning
	97	+# ---------------------------------------------------------------------------
	98	+
	99	+_PRICE_STRIP_RE = re.compile(r"[^\d.]")
	100	+
	101	+
	102	+def clean_price(
	103	+ df: pd.DataFrame,
	104	+ *,
	105	+ price_col: str = "price_numeric",
	106	+ raw_price_col: str = "price",
	107	+ floor: float = PRICE_FLOOR,
	108	+ ceiling: float = PRICE_CEILING,
	109	+ record: Optional[CleaningRecord] = None,
	110	+) -> pd.DataFrame:
	111	+ """Clean and standardise the price column.
	112	+
	113	+ Handles both the pre-parsed ``price_numeric`` column in the cleaned
	114	+ sample and the raw ``$1,234.00``-style ``price`` string column in
	115	+ full Inside Airbnb data.
	116	+
	117	+ Steps
	118	+ -----
	119	+ 1. If raw_price_col exists and price_col does not, parse the
	120	+ raw column (strip ``$``, commas, whitespace) to float.
	121	+ 2. Replace non-positive values with ``NaN``.
	122	+ 3. Replace values outside ``[floor, ceiling]`` with ``NaN``.
	123	+ """
	124	+ rec = record or _get_record()
	125	+ n = len(df)
	126	+ df = df.copy()
	127	+
	128	+ # Parse raw string price if needed
	129	+ if raw_price_col in df.columns and price_col not in df.columns:
	130	+ raw = df[raw_price_col].astype(str)
	131	+ parsed = raw.apply(lambda s: _PRICE_STRIP_RE.sub("", s))
	132	+ df[price_col] = pd.to_numeric(parsed, errors="coerce")
	133	+ failed = df[price_col].isnull().sum() - df[raw_price_col].isnull().sum()
	134	+ rec.log("clean_price", price_col, "Parsed raw price string to numeric", max(failed, 0), n)
	135	+ elif price_col not in df.columns:
	136	+ logger.warning("Neither '%s' nor '%s' found in DataFrame", price_col, raw_price_col)
	137	+ return df
	138	+
	139	+ before_missing = int(df[price_col].isnull().sum())
	140	+
	141	+ # Non-positive -> NaN
	142	+ mask_nonpos = df[price_col] <= 0
	143	+ n_nonpos = int(mask_nonpos.sum())
	144	+ if n_nonpos > 0:
	145	+ df.loc[mask_nonpos, price_col] = np.nan
	146	+ rec.log("clean_price", price_col, f"Set {n_nonpos} non-positive prices to NaN", n_nonpos, n)
	147	+
	148	+ # Below floor -> NaN
	149	+ mask_low = df[price_col] < floor
	150	+ n_low = int(mask_low.sum())
	151	+ if n_low > 0:
	152	+ df.loc[mask_low, price_col] = np.nan
	153	+ rec.log("clean_price", price_col, f"Set {n_low} prices below floor (${floor}) to NaN", n_low, n)
	154	+
	155	+ # Above ceiling -> NaN
	156	+ mask_high = df[price_col] > ceiling
	157	+ n_high = int(mask_high.sum())
	158	+ if n_high > 0:
	159	+ df.loc[mask_high, price_col] = np.nan
	160	+ rec.log("clean_price", price_col, f"Set {n_high} prices above ceiling (${ceiling:,}) to NaN", n_high, n)
	161	+
	162	+ after_missing = int(df[price_col].isnull().sum())
	163	+ total_set_nan = after_missing - before_missing
	164	+ rec.log(
	165	+ "clean_price", price_col,
	166	+ f"Total new NaN from price cleaning: {total_set_nan} "
	167	+ f"({after_missing} total missing)",
	168	+ total_set_nan, n,
	169	+ )
	170	+
	171	+ return df
	172	+
	173	+
	174	+def _winsorize_series(s: pd.Series, lower: float, upper: float) -> pd.Series:
	175	+ """Winsorize s at the given quantile bounds, ignoring NaN."""
	176	+ q_lo = s.quantile(lower)
	177	+ q_hi = s.quantile(upper)
	178	+ return s.clip(lower=q_lo, upper=q_hi)
	179	+
	180	+
	181	+def create_price_variants(
	182	+ df: pd.DataFrame,
	183	+ *,
	184	+ price_col: str = "price_numeric",
	185	+ record: Optional[CleaningRecord] = None,
	186	+) -> pd.DataFrame:
	187	+ """Create multiple price representations for modelling.
	188	+
	189	+ Columns added
	190	+ -------------
	191	+ ``price_raw``
	192	+ Copy of the cleaned numeric price.
	193	+ ``price_winsor_1_99``
	194	+ Winsorized at 1st / 99th percentile.
	195	+ ``price_winsor_2_98``
	196	+ Winsorized at 2nd / 98th percentile.
	197	+ ``price_winsor_5_95``
	198	+ Winsorized at 5th / 95th percentile.
	199	+ ``log_price_raw``
	200	+ Natural log of raw price (NaN for non-positive).
	201	+ ``log_price_winsor``
	202	+ Natural log of the 1-99 winsorized price.
	203	+ """
	204	+ rec = record or _get_record()
	205	+ n = len(df)
	206	+ df = df.copy()
	207	+
	208	+ if price_col not in df.columns:
	209	+ logger.warning("Price column '%s' not found; skipping variant creation", price_col)
	210	+ return df
	211	+
	212	+ prices = df[price_col]
	213	+
	214	+ # Raw copy
	215	+ df["price_raw"] = prices.copy()
	216	+
	217	+ # Winsorized variants
	218	+ df["price_winsor_1_99"] = _winsorize_series(prices, *WINSORIZE_1_99)
	219	+ df["price_winsor_2_98"] = _winsorize_series(prices, *WINSORIZE_2_98)
	220	+ df["price_winsor_5_95"] = _winsorize_series(prices, *WINSORIZE_5_95)
	221	+
	222	+ # Log transforms (only for positive values)
	223	+ df["log_price_raw"] = np.where(prices > 0, np.log(prices), np.nan)
	224	+ df["log_price_winsor"] = np.where(
	225	+ df["price_winsor_1_99"] > 0,
	226	+ np.log(df["price_winsor_1_99"]),
	227	+ np.nan,
	228	+ )
	229	+
	230	+ variants_created = [
	231	+ "price_raw", "price_winsor_1_99", "price_winsor_2_98",
	232	+ "price_winsor_5_95", "log_price_raw", "log_price_winsor",
	233	+ ]
	234	+ rec.log(
	235	+ "create_price_variants", price_col,
	236	+ f"Created {len(variants_created)} price variants: {variants_created}",
	237	+ n, n,
	238	+ )
	239	+
	240	+ # Log winsorization bounds for reproducibility
	241	+ valid_prices = prices.dropna()
	242	+ if len(valid_prices) > 0:
	243	+ for label, lo, hi in [
	244	+ ("1-99", *WINSORIZE_1_99),
	245	+ ("2-98", *WINSORIZE_2_98),
	246	+ ("5-95", *WINSORIZE_5_95),
	247	+ ]:
	248	+ q_lo = valid_prices.quantile(lo)
	249	+ q_hi = valid_prices.quantile(hi)
	250	+ logger.info(
	251	+ "Winsor %s bounds: $%.2f -- $%.2f", label, q_lo, q_hi
	252	+ )
	253	+
	254	+ return df
	255	+
	256	+
	257	+# ---------------------------------------------------------------------------
	258	+# 2. Rating cleaning
	259	+# ---------------------------------------------------------------------------
	260	+
	261	+
	262	+def clean_rating(
	263	+ df: pd.DataFrame,
	264	+ *,
	265	+ rating_col: str = "rating",
	266	+ raw_rating_col: str = "review_scores_rating",
	267	+ record: Optional[CleaningRecord] = None,
	268	+) -> pd.DataFrame:
	269	+ """Parse and validate rating values.
	270	+
	271	+ Handles both pre-parsed ``rating`` (0-5 scale) and the Inside
	272	+ Airbnb ``review_scores_rating`` column (may be 0-100 scale in older
	273	+ extracts or 0-5 in newer ones).
	274	+ """
	275	+ rec = record or _get_record()
	276	+ n = len(df)
	277	+ df = df.copy()
	278	+
	279	+ # If raw_rating_col exists but rating_col does not, create it
	280	+ if raw_rating_col in df.columns and rating_col not in df.columns:
	281	+ df[rating_col] = pd.to_numeric(df[raw_rating_col], errors="coerce")
	282	+ rec.log("clean_rating", rating_col, "Parsed raw rating column to numeric", n, n)
	283	+
	284	+ if rating_col not in df.columns:
	285	+ logger.info("No rating column found; skipping rating cleaning")
	286	+ return df
	287	+
	288	+ # Convert old 0-100 scale to 0-5
	289	+ if df[rating_col].max() > 5.5:
	290	+ mask_100 = df[rating_col] > 5.5
	291	+ n_converted = int(mask_100.sum())
	292	+ df.loc[mask_100, rating_col] = df.loc[mask_100, rating_col] / 20.0
	293	+ rec.log(
	294	+ "clean_rating", rating_col,
	295	+ f"Converted {n_converted} ratings from 0-100 scale to 0-5",
	296	+ n_converted, n,
	297	+ )
	298	+
	299	+ # Clip to valid range
	300	+ before_nan = int(df[rating_col].isnull().sum())
	301	+ mask_invalid = (df[rating_col] < 0) \| (df[rating_col] > 5)
	302	+ n_invalid = int(mask_invalid.sum())
	303	+ if n_invalid > 0:
	304	+ df.loc[mask_invalid, rating_col] = np.nan
	305	+ rec.log(
	306	+ "clean_rating", rating_col,
	307	+ f"Set {n_invalid} out-of-range ratings to NaN",
	308	+ n_invalid, n,
	309	+ )
	310	+
	311	+ # Create rating category if not present
	312	+ if "rating_category" not in df.columns:
	313	+ df["rating_category"] = pd.cut(
	314	+ df[rating_col],
	315	+ bins=[-np.inf, 4.0, 4.5, 4.7, 4.9, 5.01],
	316	+ labels=["Average (<4.0)", "Good (4.0-4.5)", "Very Good (4.5-4.7)",
	317	+ "Excellent (4.7-4.9)", "Exceptional (4.9-5.0)"],
	318	+ right=False,
	319	+ ).astype("object")
	320	+ df.loc[df[rating_col].isnull(), "rating_category"] = "No rating"
	321	+ rec.log("clean_rating", "rating_category", "Created rating_category from rating", n, n)
	322	+
	323	+ after_nan = int(df[rating_col].isnull().sum())
	324	+ rec.log(
	325	+ "clean_rating", rating_col,
	326	+ f"Rating cleaning complete. Missing: {after_nan} ({100*after_nan/n:.1f}%)",
	327	+ after_nan - before_nan, n,
	328	+ )
	329	+
	330	+ return df
	331	+
	332	+
	333	+# ---------------------------------------------------------------------------
	334	+# 3. Text field cleaning
	335	+# ---------------------------------------------------------------------------
	336	+
	337	+_WHITESPACE_RE = re.compile(r"\s+")
	338	+_HTML_TAG_RE = re.compile(r"<[^>]+>")
	339	+_URL_RE = re.compile(r"https?://\S+")
	340	+
	341	+
	342	+def clean_text_fields(
	343	+ df: pd.DataFrame,
	344	+ *,
	345	+ text_cols: Optional[List[str]] = None,
	346	+ record: Optional[CleaningRecord] = None,
	347	+) -> pd.DataFrame:
	348	+ """Clean free-text columns (name, description, neighborhood_overview, etc.).
	349	+
	350	+ Operations: strip HTML tags, normalise whitespace, strip leading/
	351	+ trailing whitespace, replace empty strings with NaN.
	352	+ """
	353	+ rec = record or _get_record()
	354	+ n = len(df)
	355	+ df = df.copy()
	356	+
	357	+ if text_cols is None:
	358	+ text_cols = [
	359	+ c for c in ("name", "description", "neighborhood_overview",
	360	+ "host_about", "transit", "access", "interaction",
	361	+ "house_rules", "notes")
	362	+ if c in df.columns
	363	+ ]
	364	+
	365	+ for col in text_cols:
	366	+ if col not in df.columns:
	367	+ continue
	368	+
	369	+ original_missing = int(df[col].isnull().sum())
	370	+ s = df[col].astype(str)
	371	+
	372	+ # Remove HTML
	373	+ s = s.str.replace(_HTML_TAG_RE, " ", regex=True)
	374	+ # Remove URLs
	375	+ s = s.str.replace(_URL_RE, " ", regex=True)
	376	+ # Collapse whitespace
	377	+ s = s.str.replace(_WHITESPACE_RE, " ", regex=True)
	378	+ # Strip
	379	+ s = s.str.strip()
	380	+ # Empty / 'nan' -> actual NaN
	381	+ s = s.replace({"": np.nan, "nan": np.nan, "None": np.nan, "N/A": np.nan})
	382	+
	383	+ df[col] = s
	384	+ new_missing = int(df[col].isnull().sum())
	385	+ cleaned_count = new_missing - original_missing
	386	+
	387	+ rec.log(
	388	+ "clean_text", col,
	389	+ f"Stripped HTML/URLs/whitespace; {cleaned_count} new NaN",
	390	+ max(cleaned_count, 0), n,
	391	+ )
	392	+
	393	+ return df
	394	+
	395	+
	396	+# ---------------------------------------------------------------------------
	397	+# 4. Host field cleaning
	398	+# ---------------------------------------------------------------------------
	399	+
	400	+_PCT_RE = re.compile(r"[\d.]+")
	401	+
	402	+
	403	+def _parse_pct_to_float(s: pd.Series) -> pd.Series:
	404	+ """Convert percentage strings like '87%' or '0.87' to float [0, 1]."""
	405	+ out = pd.to_numeric(s.astype(str).str.replace("%", "", regex=False), errors="coerce")
	406	+ # If values are > 1, assume they are 0-100 scale
	407	+ mask_pct = out > 1.0
	408	+ out = out.where(~mask_pct, out / 100.0)
	409	+ return out
	410	+
	411	+
	412	+def clean_host_fields(
	413	+ df: pd.DataFrame,
	414	+ *,
	415	+ record: Optional[CleaningRecord] = None,
	416	+) -> pd.DataFrame:
	417	+ """Parse host_response_rate, host_acceptance_rate to float [0, 1].
	418	+
	419	+ Also converts host_is_superhost / host_identity_verified from
	420	+ 't'/'f' strings to boolean if needed.
	421	+ """
	422	+ rec = record or _get_record()
	423	+ n = len(df)
	424	+ df = df.copy()
	425	+
	426	+ # Response / acceptance rates
	427	+ for col in ("host_response_rate", "host_acceptance_rate"):
	428	+ if col not in df.columns:
	429	+ continue
	430	+ before_valid = int(df[col].notna().sum())
	431	+ df[col] = _parse_pct_to_float(df[col])
	432	+ # Clip to [0, 1]
	433	+ df[col] = df[col].clip(0.0, 1.0)
	434	+ after_valid = int(df[col].notna().sum())
	435	+ rec.log(
	436	+ "clean_host", col,
	437	+ f"Parsed to float [0,1]. Valid: {before_valid} -> {after_valid}",
	438	+ abs(after_valid - before_valid), n,
	439	+ )
	440	+
	441	+ # Boolean-like host columns ('t'/'f' -> True/False)
	442	+ bool_cols = [
	443	+ "host_is_superhost", "host_identity_verified",
	444	+ "host_has_profile_pic",
	445	+ ]
	446	+ for col in bool_cols:
	447	+ if col not in df.columns:
	448	+ continue
	449	+ if df[col].dtype == "bool":
	450	+ continue
	451	+ mapping = {"t": True, "f": False, "True": True, "False": False}
	452	+ df[col] = df[col].map(mapping)
	453	+ rec.log("clean_host", col, "Converted t/f strings to boolean", n, n)
	454	+
	455	+ # is_superhost: unify from host_is_superhost if our column is missing
	456	+ if "is_superhost" not in df.columns and "host_is_superhost" in df.columns:
	457	+ df["is_superhost"] = df["host_is_superhost"].fillna(False).astype(bool)
	458	+ rec.log("clean_host", "is_superhost", "Created from host_is_superhost", n, n)
	459	+
	460	+ # host_listings_count -> numeric
	461	+ if "host_listings_count" in df.columns:
	462	+ df["host_listings_count"] = pd.to_numeric(
	463	+ df["host_listings_count"], errors="coerce"
	464	+ )
	465	+
	466	+ # host_total_listings_count -> numeric
	467	+ if "host_total_listings_count" in df.columns:
	468	+ df["host_total_listings_count"] = pd.to_numeric(
	469	+ df["host_total_listings_count"], errors="coerce"
	470	+ )
	471	+
	472	+ return df
	473	+
	474	+
	475	+# ---------------------------------------------------------------------------
	476	+# 5. Coordinate cleaning
	477	+# ---------------------------------------------------------------------------
	478	+
	479	+
	480	+def clean_coordinates(
	481	+ df: pd.DataFrame,
	482	+ *,
	483	+ lat_col: str = "lat",
	484	+ lon_col: str = "long",
	485	+ lat_range: Tuple[float, float] = QUEBEC_LAT_RANGE,
	486	+ lon_range: Tuple[float, float] = QUEBEC_LON_RANGE,
	487	+ record: Optional[CleaningRecord] = None,
	488	+) -> pd.DataFrame:
	489	+ """Validate and clean geographic coordinates for Quebec listings.
	490	+
	491	+ Also handles the Inside Airbnb column names ``latitude`` /
	492	+ ``longitude`` by renaming them to ``lat`` / ``long``.
	493	+ """
	494	+ rec = record or _get_record()
	495	+ n = len(df)
	496	+ df = df.copy()
	497	+
	498	+ # Rename full names to short aliases if needed
	499	+ rename_map: Dict[str, str] = {}
	500	+ if "latitude" in df.columns and lat_col not in df.columns:
	501	+ rename_map["latitude"] = lat_col
	502	+ if "longitude" in df.columns and lon_col not in df.columns:
	503	+ rename_map["longitude"] = lon_col
	504	+ if rename_map:
	505	+ df.rename(columns=rename_map, inplace=True)
	506	+ rec.log(
	507	+ "clean_coords", "rename",
	508	+ f"Renamed columns: {rename_map}", len(rename_map), n,
	509	+ )
	510	+
	511	+ if lat_col not in df.columns or lon_col not in df.columns:
	512	+ logger.warning("Coordinate columns not found; skipping coordinate cleaning")
	513	+ return df
	514	+
	515	+ # Ensure numeric
	516	+ df[lat_col] = pd.to_numeric(df[lat_col], errors="coerce")
	517	+ df[lon_col] = pd.to_numeric(df[lon_col], errors="coerce")
	518	+
	519	+ # Flag out-of-range
	520	+ lat_min, lat_max = lat_range
	521	+ lon_min, lon_max = lon_range
	522	+
	523	+ mask_bad_lat = (df[lat_col] < lat_min) \| (df[lat_col] > lat_max)
	524	+ mask_bad_lon = (df[lon_col] < lon_min) \| (df[lon_col] > lon_max)
	525	+ mask_bad = mask_bad_lat \| mask_bad_lon
	526	+ n_bad = int(mask_bad.sum())
	527	+
	528	+ if n_bad > 0:
	529	+ df.loc[mask_bad, lat_col] = np.nan
	530	+ df.loc[mask_bad, lon_col] = np.nan
	531	+ rec.log(
	532	+ "clean_coords", f"{lat_col}/{lon_col}",
	533	+ f"Set {n_bad} out-of-bounds coordinates to NaN "
	534	+ f"(lat: [{lat_min}, {lat_max}], lon: [{lon_min}, {lon_max}])",
	535	+ n_bad, n,
	536	+ )
	537	+
	538	+ # Flag missing coordinates
	539	+ mask_missing = df[lat_col].isnull() \| df[lon_col].isnull()
	540	+ n_missing = int(mask_missing.sum())
	541	+ rec.log(
	542	+ "clean_coords", f"{lat_col}/{lon_col}",
	543	+ f"Total missing coordinates after cleaning: {n_missing}",
	544	+ n_missing, n,
	545	+ )
	546	+
	547	+ return df
	548	+
	549	+
	550	+# ---------------------------------------------------------------------------
	551	+# 6. Property type standardisation
	552	+# ---------------------------------------------------------------------------
	553	+
	554	+
	555	+def clean_property_type(
	556	+ df: pd.DataFrame,
	557	+ *,
	558	+ col: str = "property_type",
	559	+ mapping: Optional[Dict[str, str]] = None,
	560	+ record: Optional[CleaningRecord] = None,
	561	+) -> pd.DataFrame:
	562	+ """Standardise property types to a canonical set.
	563	+
	564	+ Uses ``PROPERTY_TYPE_MAP`` from config by default. Unmapped values
	565	+ are assigned ``"Other"``.
	566	+ """
	567	+ rec = record or _get_record()
	568	+ n = len(df)
	569	+ df = df.copy()
	570	+
	571	+ if col not in df.columns:
	572	+ logger.info("Column '%s' not found; skipping property type cleaning", col)
	573	+ return df
	574	+
	575	+ mapping = mapping or PROPERTY_TYPE_MAP
	576	+ original_unique = df[col].nunique()
	577	+
	578	+ # Map known types; assign "Other" to unknowns
	579	+ df[col] = df[col].map(mapping).fillna("Other")
	580	+
	581	+ new_unique = df[col].nunique()
	582	+ mapped_to_other = int((df[col] == "Other").sum())
	583	+
	584	+ rec.log(
	585	+ "clean_property_type", col,
	586	+ f"Standardised {original_unique} types -> {new_unique} types. "
	587	+ f"{mapped_to_other} mapped to 'Other'",
	588	+ n, n,
	589	+ )
	590	+
	591	+ return df
	592	+
	593	+
	594	+# ---------------------------------------------------------------------------
	595	+# 7. Room type standardisation
	596	+# ---------------------------------------------------------------------------
	597	+
	598	+
	599	+def clean_room_type(
	600	+ df: pd.DataFrame,
	601	+ *,
	602	+ col: str = "room_type",
	603	+ mapping: Optional[Dict[str, str]] = None,
	604	+ record: Optional[CleaningRecord] = None,
	605	+) -> pd.DataFrame:
	606	+ """Standardise room types to the canonical four-level set.
	607	+
	608	+ Maps case-insensitive variants to:
	609	+ ``Entire home/apt``, ``Private room``, ``Shared room``, ``Hotel room``.
	610	+ """
	611	+ rec = record or _get_record()
	612	+ n = len(df)
	613	+ df = df.copy()
	614	+
	615	+ if col not in df.columns:
	616	+ logger.info("Column '%s' not found; skipping room type cleaning", col)
	617	+ return df
	618	+
	619	+ mapping = mapping or ROOM_TYPE_MAP
	620	+ original_unique = df[col].nunique()
	621	+
	622	+ # Try direct map first, then lower-case map
	623	+ mapped = df[col].map(mapping)
	624	+ still_null = mapped.isnull()
	625	+ if still_null.any():
	626	+ lower_mapped = df.loc[still_null, col].str.lower().str.strip().map(mapping)
	627	+ mapped = mapped.fillna(lower_mapped)
	628	+
	629	+ # Anything still unmapped -> try substring matching
	630	+ still_null = mapped.isnull()
	631	+ if still_null.any():
	632	+ for idx in df.loc[still_null].index:
	633	+ raw = str(df.at[idx, col]).lower()
	634	+ if "entire" in raw:
	635	+ mapped.at[idx] = "Entire home/apt"
	636	+ elif "private" in raw:
	637	+ mapped.at[idx] = "Private room"
	638	+ elif "shared" in raw:
	639	+ mapped.at[idx] = "Shared room"
	640	+ elif "hotel" in raw:
	641	+ mapped.at[idx] = "Hotel room"
	642	+ else:
	643	+ mapped.at[idx] = "Entire home/apt" # default
	644	+
	645	+ df[col] = mapped
	646	+ new_unique = df[col].nunique()
	647	+
	648	+ rec.log(
	649	+ "clean_room_type", col,
	650	+ f"Standardised {original_unique} room types -> {new_unique}",
	651	+ n, n,
	652	+ )
	653	+
	654	+ return df
	655	+
	656	+
	657	+# ---------------------------------------------------------------------------
	658	+# 8. Additional numeric cleaning helpers
	659	+# ---------------------------------------------------------------------------
	660	+
	661	+
	662	+def _ensure_numeric(
	663	+ df: pd.DataFrame,
	664	+ cols: List[str],
	665	+ record: Optional[CleaningRecord] = None,
	666	+) -> pd.DataFrame:
	667	+ """Coerce columns to numeric, logging conversion failures."""
	668	+ rec = record or _get_record()
	669	+ n = len(df)
	670	+ for col in cols:
	671	+ if col not in df.columns:
	672	+ continue
	673	+ before_na = int(df[col].isnull().sum())
	674	+ df[col] = pd.to_numeric(df[col], errors="coerce")
	675	+ after_na = int(df[col].isnull().sum())
	676	+ new_na = after_na - before_na
	677	+ if new_na > 0:
	678	+ rec.log(
	679	+ "ensure_numeric", col,
	680	+ f"Coerced to numeric; {new_na} new NaN from unparseable values",
	681	+ new_na, n,
	682	+ )
	683	+ return df
	684	+
	685	+
	686	+def _clean_boolean(
	687	+ df: pd.DataFrame,
	688	+ cols: List[str],
	689	+ record: Optional[CleaningRecord] = None,
	690	+) -> pd.DataFrame:
	691	+ """Convert t/f/True/False/1/0 columns to proper bool dtype."""
	692	+ rec = record or _get_record()
	693	+ n = len(df)
	694	+ truthy = {"t", "true", "1", "yes", "True"}
	695	+ for col in cols:
	696	+ if col not in df.columns:
	697	+ continue
	698	+ if df[col].dtype == "bool":
	699	+ continue
	700	+ df[col] = df[col].astype(str).str.strip().str.lower().isin(truthy)
	701	+ rec.log("clean_boolean", col, "Converted to boolean dtype", n, n)
	702	+ return df
	703	+
	704	+
	705	+# ---------------------------------------------------------------------------
	706	+# 9. Inside Airbnb amenities parsing
	707	+# ---------------------------------------------------------------------------
	708	+
	709	+
	710	+def parse_amenities(
	711	+ df: pd.DataFrame,
	712	+ *,
	713	+ col: str = "amenities",
	714	+ record: Optional[CleaningRecord] = None,
	715	+) -> pd.DataFrame:
	716	+ """Parse the JSON-like amenities string into count and flag columns.
	717	+
	718	+ Inside Airbnb stores amenities as: ``'["Wifi", "Kitchen", ...]'``.
	719	+ We extract the count and create binary flags for key amenities.
	720	+ """
	721	+ rec = record or _get_record()
	722	+ n = len(df)
	723	+ df = df.copy()
	724	+
	725	+ if col not in df.columns:
	726	+ logger.info("Column '%s' not found; skipping amenity parsing", col)
	727	+ return df
	728	+
	729	+ def _count_amenities(val: Any) -> int:
	730	+ if pd.isnull(val):
	731	+ return 0
	732	+ s = str(val)
	733	+ # Count quoted items
	734	+ items = re.findall(r'"([^"]+)"', s)
	735	+ return len(items) if items else 0
	736	+
	737	+ def _has_amenity(val: Any, keyword: str) -> bool:
	738	+ if pd.isnull(val):
	739	+ return False
	740	+ return keyword.lower() in str(val).lower()
	741	+
	742	+ if "amenities_count" not in df.columns:
	743	+ df["amenities_count"] = df[col].apply(_count_amenities)
	744	+ rec.log("parse_amenities", "amenities_count", "Counted amenities from raw string", n, n)
	745	+
	746	+ # Key amenity flags
	747	+ key_amenities = {
	748	+ "has_wifi": "wifi",
	749	+ "has_kitchen": "kitchen",
	750	+ "has_parking": "parking",
	751	+ "has_pool": "pool",
	752	+ "has_ac": "air conditioning",
	753	+ "has_heating": "heating",
	754	+ "has_washer": "washer",
	755	+ "has_dryer": "dryer",
	756	+ "has_tv": "tv",
	757	+ "has_hot_tub": "hot tub",
	758	+ "has_fireplace": "fireplace",
	759	+ "has_elevator": "elevator",
	760	+ "has_gym": "gym",
	761	+ }
	762	+
	763	+ created_flags: List[str] = []
	764	+ for flag_col, keyword in key_amenities.items():
	765	+ if flag_col not in df.columns:
	766	+ df[flag_col] = df[col].apply(lambda v, kw=keyword: _has_amenity(v, kw))
	767	+ created_flags.append(flag_col)
	768	+
	769	+ if created_flags:
	770	+ rec.log(
	771	+ "parse_amenities", "amenity_flags",
	772	+ f"Created {len(created_flags)} binary amenity flags",
	773	+ n, n,
	774	+ )
	775	+
	776	+ return df
	777	+
	778	+
	779	+# ---------------------------------------------------------------------------
	780	+# 10. Master cleaning pipeline
	781	+# ---------------------------------------------------------------------------
	782	+
	783	+
	784	+def run_full_cleaning(
	785	+ df: pd.DataFrame,
	786	+ *,
	787	+ price_floor: float = PRICE_FLOOR,
	788	+ price_ceiling: float = PRICE_CEILING,
	789	+ create_variants: bool = True,
	790	+ parse_amenities_col: bool = True,
	791	+ verbose: bool = True,
	792	+) -> pd.DataFrame:
	793	+ """Execute the full cleaning pipeline in the canonical order.
	794	+
	795	+ Parameters
	796	+ ----------
	797	+ df : pd.DataFrame
	798	+ Raw or semi-cleaned listings data.
	799	+ price_floor / price_ceiling : float
	800	+ Plausible nightly price bounds (CAD).
	801	+ create_variants : bool
	802	+ Whether to create winsorized / log price columns.
	803	+ parse_amenities_col : bool
	804	+ Whether to parse the raw amenities string (if present).
	805	+ verbose : bool
	806	+ Extra logging.
	807	+
	808	+ Returns
	809	+ -------
	810	+ pd.DataFrame
	811	+ Cleaned DataFrame with all new columns appended.
	812	+ """
	813	+ global _record
	814	+ _record = CleaningRecord()
	815	+ rec = _record
	816	+
	817	+ n_original = len(df)
	818	+ logger.info("=" * 70)
	819	+ logger.info("STARTING FULL CLEANING PIPELINE \| %s rows \| %s cols", f"{n_original:,}", len(df.columns))
	820	+ logger.info("=" * 70)
	821	+
	822	+ # 1. Text fields
	823	+ df = clean_text_fields(df, record=rec)
	824	+
	825	+ # 2. Host fields (before price so is_superhost is available)
	826	+ df = clean_host_fields(df, record=rec)
	827	+
	828	+ # 3. Prices
	829	+ df = clean_price(df, floor=price_floor, ceiling=price_ceiling, record=rec)
	830	+ if create_variants:
	831	+ df = create_price_variants(df, record=rec)
	832	+
	833	+ # 4. Ratings
	834	+ df = clean_rating(df, record=rec)
	835	+
	836	+ # 5. Coordinates
	837	+ df = clean_coordinates(df, record=rec)
	838	+
	839	+ # 6. Property type
	840	+ df = clean_property_type(df, record=rec)
	841	+
	842	+ # 7. Room type
	843	+ df = clean_room_type(df, record=rec)
	844	+
	845	+ # 8. Amenities (raw string parsing)
	846	+ if parse_amenities_col:
	847	+ df = parse_amenities(df, record=rec)
	848	+
	849	+ # 9. Ensure key numeric columns
	850	+ numeric_cols = [
	851	+ "bedrooms", "bathrooms", "guests_count", "accommodates",
	852	+ "beds", "amenities_count", "num_images", "num_reviews",
	853	+ "quality_score", "number_of_reviews",
	854	+ "minimum_nights", "maximum_nights",
	855	+ "availability_30", "availability_60", "availability_90", "availability_365",
	856	+ "reviews_per_month", "calculated_host_listings_count",
	857	+ ]
	858	+ df = _ensure_numeric(df, [c for c in numeric_cols if c in df.columns], record=rec)
	859	+
	860	+ # 10. Ensure boolean columns
	861	+ bool_cols = ["is_superhost", "is_guest_favorite", "pets_allowed",
	862	+ "instant_bookable"]
	863	+ df = _clean_boolean(df, [c for c in bool_cols if c in df.columns], record=rec)
	864	+
	865	+ # Summary
	866	+ n_final = len(df)
	867	+ n_cols_final = len(df.columns)
	868	+ logger.info("=" * 70)
	869	+ logger.info(
	870	+ "CLEANING COMPLETE \| %s rows \| %s cols \| %s actions logged",
	871	+ f"{n_final:,}", n_cols_final, len(rec.actions),
	872	+ )
	873	+ logger.info("=" * 70)
	874	+
	875	+ return df
	876	+
	877	+
	878	+# ---------------------------------------------------------------------------
	879	+# 11. Cleaning report
	880	+# ---------------------------------------------------------------------------
	881	+
	882	+
	883	+def generate_cleaning_report(
	884	+ df_before: pd.DataFrame,
	885	+ df_after: pd.DataFrame,
	886	+ *,
	887	+ record: Optional[CleaningRecord] = None,
	888	+) -> Dict[str, Any]:
	889	+ """Generate a comprehensive report comparing pre- and post-cleaning data.
	890	+
	891	+ Parameters
	892	+ ----------
	893	+ df_before : pd.DataFrame
	894	+ The DataFrame before cleaning.
	895	+ df_after : pd.DataFrame
	896	+ The DataFrame after cleaning.
	897	+ record : CleaningRecord, optional
	898	+ The cleaning audit trail; defaults to the module-level record.
	899	+
	900	+ Returns
	901	+ -------
	902	+ dict
	903	+ A structured report suitable for logging or serialisation.
	904	+ """
	905	+ rec = record or _get_record()
	906	+
	907	+ report: Dict[str, Any] = {
	908	+ "rows_before": len(df_before),
	909	+ "rows_after": len(df_after),
	910	+ "rows_dropped": len(df_before) - len(df_after),
	911	+ "cols_before": len(df_before.columns),
	912	+ "cols_after": len(df_after.columns),
	913	+ "cols_added": sorted(set(df_after.columns) - set(df_before.columns)),
	914	+ "cols_removed": sorted(set(df_before.columns) - set(df_after.columns)),
	915	+ }
	916	+
	917	+ # Missing value comparison
	918	+ missing_before: Dict[str, int] = {}
	919	+ missing_after: Dict[str, int] = {}
	920	+ for col in df_before.columns:
	921	+ missing_before[col] = int(df_before[col].isnull().sum())
	922	+ for col in df_after.columns:
	923	+ missing_after[col] = int(df_after[col].isnull().sum())
	924	+ report["missing_before"] = missing_before
	925	+ report["missing_after"] = missing_after
	926	+
	927	+ # Price comparison
	928	+ if "price_numeric" in df_before.columns and "price_numeric" in df_after.columns:
	929	+ p_before = df_before["price_numeric"].dropna()
	930	+ p_after = df_after["price_numeric"].dropna()
	931	+ report["price_comparison"] = {
	932	+ "valid_before": len(p_before),
	933	+ "valid_after": len(p_after),
	934	+ "mean_before": float(p_before.mean()) if len(p_before) > 0 else None,
	935	+ "mean_after": float(p_after.mean()) if len(p_after) > 0 else None,
	936	+ "median_before": float(p_before.median()) if len(p_before) > 0 else None,
	937	+ "median_after": float(p_after.median()) if len(p_after) > 0 else None,
	938	+ "std_before": float(p_before.std()) if len(p_before) > 0 else None,
	939	+ "std_after": float(p_after.std()) if len(p_after) > 0 else None,
	940	+ "min_before": float(p_before.min()) if len(p_before) > 0 else None,
	941	+ "min_after": float(p_after.min()) if len(p_after) > 0 else None,
	942	+ "max_before": float(p_before.max()) if len(p_before) > 0 else None,
	943	+ "max_after": float(p_after.max()) if len(p_after) > 0 else None,
	944	+ }
	945	+
	946	+ # Cleaning actions log
	947	+ report["actions"] = rec.actions
	948	+ report["total_actions"] = len(rec.actions)
	949	+
	950	+ # Log the report highlights
	951	+ logger.info("-" * 70)
	952	+ logger.info("CLEANING REPORT")
	953	+ logger.info("-" * 70)
	954	+ logger.info("Rows: %s -> %s (dropped %s)", f"{report['rows_before']:,}",
	955	+ f"{report['rows_after']:,}", report["rows_dropped"])
	956	+ logger.info("Columns: %s -> %s", report["cols_before"], report["cols_after"])
	957	+ if report["cols_added"]:
	958	+ logger.info("Columns added: %s", report["cols_added"])
	959	+ if report["cols_removed"]:
	960	+ logger.info("Columns removed: %s", report["cols_removed"])
	961	+ logger.info("Total cleaning actions: %s", report["total_actions"])
	962	+ logger.info("-" * 70)
	963	+
	964	+ return report

added src/config.py +322 −0

		`@@ -0,0 +1,322 @@`
	1	+"""
	2	+config.py -- Project-wide configuration for hedonic pricing analysis.
	3	+
	4	+Centralises every tunable constant, file path, spatial reference,
	5	+modelling parameter, and visual-style setting so that downstream
	6	+modules never hard-code values.
	7	+"""
	8	+
	9	+from __future__ import annotations
	10	+
	11	+import os
	12	+from dataclasses import dataclass, field
	13	+from pathlib import Path
	14	+from typing import Any, Dict, List, Tuple
	15	+
	16	+# ---------------------------------------------------------------------------
	17	+# 1. Directory layout
	18	+# ---------------------------------------------------------------------------
	19	+
	20	+PROJECT_ROOT: Path = Path(__file__).resolve().parent.parent
	21	+
	22	+# Data directories
	23	+DATA_DIR: Path = PROJECT_ROOT / "data"
	24	+DATA_RAW: Path = DATA_DIR / "raw"
	25	+DATA_INTERIM: Path = DATA_DIR / "interim"
	26	+DATA_PROCESSED: Path = DATA_DIR / "processed"
	27	+DATA_DUCKDB: Path = DATA_DIR / "duckdb"
	28	+
	29	+# Output directories
	30	+OUTPUTS_DIR: Path = PROJECT_ROOT / "results"
	31	+OUTPUTS_TABLES: Path = OUTPUTS_DIR / "tables"
	32	+OUTPUTS_FIGURES: Path = OUTPUTS_DIR / "figures"
	33	+OUTPUTS_MODELS: Path = OUTPUTS_DIR / "models"
	34	+
	35	+# Paper / LaTeX directory
	36	+PAPER_DIR: Path = PROJECT_ROOT / "paper"
	37	+
	38	+# Ensure all directories exist at import time (idempotent)
	39	+for _d in (
	40	+ DATA_RAW, DATA_INTERIM, DATA_PROCESSED, DATA_DUCKDB,
	41	+ OUTPUTS_TABLES, OUTPUTS_FIGURES, OUTPUTS_MODELS, PAPER_DIR,
	42	+):
	43	+ _d.mkdir(parents=True, exist_ok=True)
	44	+
	45	+# ---------------------------------------------------------------------------
	46	+# 2. Raw data file paths
	47	+# ---------------------------------------------------------------------------
	48	+
	49	+# Primary cleaned sample (always present)
	50	+CLEANED_SAMPLE_CSV: Path = PROJECT_ROOT / "data" / "raw" / "cleaned_dataset_sample.csv"
	51	+
	52	+# Inside Airbnb full extracts (may or may not exist)
	53	+LISTINGS_CSV_GZ: Path = DATA_RAW / "listings.csv.gz"
	54	+CALENDAR_CSV_GZ: Path = DATA_RAW / "calendar.csv.gz"
	55	+REVIEWS_CSV_GZ: Path = DATA_RAW / "reviews.csv.gz"
	56	+NEIGHBOURHOODS_CSV: Path = DATA_RAW / "neighbourhoods.csv"
	57	+NEIGHBOURHOODS_GEOJSON: Path = DATA_RAW / "neighbourhoods.geojson"
	58	+
	59	+# DuckDB database
	60	+DUCKDB_PATH: Path = DATA_DUCKDB / "airbnb.duckdb"
	61	+
	62	+# ---------------------------------------------------------------------------
	63	+# 3. Spatial / geographic constants
	64	+# ---------------------------------------------------------------------------
	65	+
	66	+@dataclass(frozen=True)
	67	+class CityCenter:
	68	+ """WGS-84 centroid of a Quebec city."""
	69	+ name: str
	70	+ lat: float
	71	+ lon: float
	72	+
	73	+
	74	+CITY_CENTERS: Dict[str, CityCenter] = {
	75	+ "Montréal": CityCenter("Montréal", 45.5017, -73.5673),
	76	+ "Montreal": CityCenter("Montréal", 45.5017, -73.5673),
	77	+ "Québec": CityCenter("Québec", 46.8139, -71.2080),
	78	+ "Quebec": CityCenter("Québec", 46.8139, -71.2080),
	79	+ "Gatineau": CityCenter("Gatineau", 45.4765, -75.7013),
	80	+ "Sherbrooke": CityCenter("Sherbrooke", 45.4042, -71.8929),
	81	+ "Laval": CityCenter("Laval", 45.6066, -73.7124),
	82	+ "Trois-Rivières": CityCenter("Trois-Rivières", 46.3432, -72.5432),
	83	+ "Trois-Rivieres": CityCenter("Trois-Rivières", 46.3432, -72.5432),
	84	+ "Saguenay": CityCenter("Saguenay", 48.4279, -71.0548),
	85	+ "Lévis": CityCenter("Lévis", 46.8032, -71.1780),
	86	+ "Levis": CityCenter("Lévis", 46.8032, -71.1780),
	87	+ "Terrebonne": CityCenter("Terrebonne", 45.7005, -73.6474),
	88	+ "Longueuil": CityCenter("Longueuil", 45.5312, -73.5185),
	89	+ "Rimouski": CityCenter("Rimouski", 48.4489, -68.5234),
	90	+ "Saint-Hyacinthe": CityCenter("Saint-Hyacinthe", 45.6307, -72.9570),
	91	+ "Drummondville": CityCenter("Drummondville", 45.8838, -72.4843),
	92	+ "Granby": CityCenter("Granby", 45.4001, -72.7329),
	93	+ "Mont-Tremblant": CityCenter("Mont-Tremblant", 46.2088, -74.5849),
	94	+ "Magog": CityCenter("Magog", 45.2668, -72.1419),
	95	+ "Charlevoix": CityCenter("Charlevoix", 47.4389, -70.5025),
	96	+}
	97	+
	98	+# Quebec bounding box (generous, covers all of southern Quebec + some northern)
	99	+QUEBEC_LAT_RANGE: Tuple[float, float] = (44.5, 55.5)
	100	+QUEBEC_LON_RANGE: Tuple[float, float] = (-80.0, -56.0)
	101	+
	102	+# Spatial buffer distances in metres
	103	+SPATIAL_BUFFERS_M: List[int] = [250, 500, 1_000, 2_000]
	104	+
	105	+# Earth radius for haversine (km)
	106	+EARTH_RADIUS_KM: float = 6_371.0
	107	+
	108	+# ---------------------------------------------------------------------------
	109	+# 4. Price & winsorization
	110	+# ---------------------------------------------------------------------------
	111	+
	112	+# Winsorization percentile pairs (lower, upper)
	113	+WINSORIZE_1_99: Tuple[float, float] = (0.01, 0.99)
	114	+WINSORIZE_2_98: Tuple[float, float] = (0.02, 0.98)
	115	+WINSORIZE_5_95: Tuple[float, float] = (0.05, 0.95)
	116	+
	117	+# Default winsorization used in modelling
	118	+DEFAULT_WINSORIZE: Tuple[float, float] = WINSORIZE_1_99
	119	+
	120	+# Minimum valid nightly price in CAD
	121	+PRICE_FLOOR: float = 10.0
	122	+
	123	+# Maximum plausible nightly price in CAD
	124	+PRICE_CEILING: float = 25_000.0
	125	+
	126	+# ---------------------------------------------------------------------------
	127	+# 5. Quantile regression thresholds
	128	+# ---------------------------------------------------------------------------
	129	+
	130	+QUANTILE_THRESHOLDS: List[float] = [0.10, 0.25, 0.50, 0.75, 0.90]
	131	+
	132	+# ---------------------------------------------------------------------------
	133	+# 6. Feature groups
	134	+# ---------------------------------------------------------------------------
	135	+
	136	+FEATURE_GROUPS: Dict[str, List[str]] = {
	137	+ "structural": [
	138	+ "bedrooms", "bathrooms", "guests_count", "amenities_count",
	139	+ ],
	140	+ "location": [
	141	+ "lat", "long", "dist_city_center_km",
	142	+ ],
	143	+ "quality": [
	144	+ "rating", "quality_score", "num_reviews", "num_images",
	145	+ ],
	146	+ "host": [
	147	+ "is_superhost", "host_response_rate", "host_acceptance_rate",
	148	+ "host_listings_count",
	149	+ ],
	150	+ "property": [
	151	+ "property_type", "room_type",
	152	+ ],
	153	+ "amenity_flags": [
	154	+ "pets_allowed", "is_guest_favorite",
	155	+ ],
	156	+}
	157	+
	158	+# Flat list of all feature names (order-preserving)
	159	+ALL_FEATURES: List[str] = [
	160	+ feat for group in FEATURE_GROUPS.values() for feat in group
	161	+]
	162	+
	163	+# ---------------------------------------------------------------------------
	164	+# 7. Property / room type mappings
	165	+# ---------------------------------------------------------------------------
	166	+
	167	+PROPERTY_TYPE_MAP: Dict[str, str] = {
	168	+ "Rental unit": "Rental unit",
	169	+ "House": "House",
	170	+ "Cabin/Chalet": "Cabin/Chalet",
	171	+ "Condo": "Condo",
	172	+ "Apartment": "Apartment",
	173	+ "Other": "Other",
	174	+ # Inside Airbnb variants
	175	+ "Entire rental unit": "Rental unit",
	176	+ "Private room in rental unit": "Rental unit",
	177	+ "Entire home": "House",
	178	+ "Entire house": "House",
	179	+ "Entire cabin": "Cabin/Chalet",
	180	+ "Entire chalet": "Cabin/Chalet",
	181	+ "Entire condo": "Condo",
	182	+ "Entire condominium (condo)": "Condo",
	183	+ "Entire loft": "Apartment",
	184	+ "Entire guest suite": "Apartment",
	185	+ "Entire guesthouse": "House",
	186	+ "Entire townhouse": "House",
	187	+ "Entire cottage": "Cabin/Chalet",
	188	+ "Entire bungalow": "House",
	189	+ "Entire villa": "House",
	190	+ "Tiny home": "Other",
	191	+ "Treehouse": "Other",
	192	+ "Boat": "Other",
	193	+ "Camper/RV": "Other",
	194	+ "Farm stay": "Other",
	195	+ "Yurt": "Other",
	196	+ "Tent": "Other",
	197	+ "Castle": "Other",
	198	+ "Private room": "Other",
	199	+ "Shared room": "Other",
	200	+}
	201	+
	202	+ROOM_TYPE_MAP: Dict[str, str] = {
	203	+ "Entire home/apt": "Entire home/apt",
	204	+ "Private room": "Private room",
	205	+ "Shared room": "Shared room",
	206	+ "Hotel room": "Hotel room",
	207	+ # Normalised aliases
	208	+ "entire home": "Entire home/apt",
	209	+ "entire place": "Entire home/apt",
	210	+ "private": "Private room",
	211	+ "shared": "Shared room",
	212	+ "hotel": "Hotel room",
	213	+}
	214	+
	215	+# ---------------------------------------------------------------------------
	216	+# 8. Model catalogue
	217	+# ---------------------------------------------------------------------------
	218	+
	219	+MODEL_NAMES: List[str] = [
	220	+ "ols_linear",
	221	+ "ols_log",
	222	+ "ols_semilog",
	223	+ "robust_huber",
	224	+ "robust_bisquare",
	225	+ "quantile_10",
	226	+ "quantile_25",
	227	+ "quantile_50",
	228	+ "quantile_75",
	229	+ "quantile_90",
	230	+ "spatial_lag",
	231	+ "spatial_error",
	232	+ "gwr",
	233	+ "rf_regressor",
	234	+ "gradient_boosting",
	235	+ "xgboost",
	236	+ "lasso",
	237	+ "ridge",
	238	+ "elastic_net",
	239	+]
	240	+
	241	+# ---------------------------------------------------------------------------
	242	+# 9. Reproducibility
	243	+# ---------------------------------------------------------------------------
	244	+
	245	+RANDOM_SEED: int = 42
	246	+TEST_SIZE: float = 0.20
	247	+CV_FOLDS: int = 5
	248	+
	249	+# ---------------------------------------------------------------------------
	250	+# 10. Visualisation palettes & figure settings
	251	+# ---------------------------------------------------------------------------
	252	+
	253	+# Colour palettes
	254	+PALETTE_SEQUENTIAL: str = "YlOrRd"
	255	+PALETTE_DIVERGING: str = "RdBu_r"
	256	+PALETTE_CATEGORICAL: List[str] = [
	257	+ "#1f77b4", # muted blue
	258	+ "#ff7f0e", # safety orange
	259	+ "#2ca02c", # cooked-asparagus green
	260	+ "#d62728", # brick red
	261	+ "#9467bd", # muted purple
	262	+ "#8c564b", # chestnut brown
	263	+ "#e377c2", # raspberry-yoghurt pink
	264	+ "#7f7f7f", # middle grey
	265	+ "#bcbd22", # curry yellow-green
	266	+ "#17becf", # blue-teal
	267	+]
	268	+
	269	+PALETTE_PROPERTY_TYPE: Dict[str, str] = {
	270	+ "Rental unit": "#1f77b4",
	271	+ "House": "#2ca02c",
	272	+ "Cabin/Chalet": "#8c564b",
	273	+ "Condo": "#ff7f0e",
	274	+ "Apartment": "#9467bd",
	275	+ "Other": "#7f7f7f",
	276	+}
	277	+
	278	+PALETTE_PRICE_CATEGORY: Dict[str, str] = {
	279	+ "Budget (<$100)": "#2ca02c",
	280	+ "Moderate ($100-200)": "#1f77b4",
	281	+ "Premium ($200-400)": "#ff7f0e",
	282	+ "Luxury ($400-1000)": "#d62728",
	283	+ "Ultra-Luxury (>$1000)": "#9467bd",
	284	+ "Unknown": "#7f7f7f",
	285	+}
	286	+
	287	+# Figure defaults
	288	+FIGURE_DPI: int = 300
	289	+FIGURE_FORMAT: str = "pdf"
	290	+FIGURE_SIZE_SINGLE: Tuple[float, float] = (6.5, 4.5)
	291	+FIGURE_SIZE_DOUBLE: Tuple[float, float] = (13.0, 4.5)
	292	+FIGURE_SIZE_FULL: Tuple[float, float] = (13.0, 9.0)
	293	+FIGURE_FONT_FAMILY: str = "serif"
	294	+FIGURE_FONT_SIZE: int = 11
	295	+
	296	+# ---------------------------------------------------------------------------
	297	+# 11. LaTeX table formatting
	298	+# ---------------------------------------------------------------------------
	299	+
	300	+@dataclass(frozen=True)
	301	+class LaTeXSettings:
	302	+ """Settings for stargazer-style LaTeX table output."""
	303	+ float_format: str = "%.4f"
	304	+ significance_levels: Tuple[float, ...] = (0.01, 0.05, 0.10)
	305	+ significance_symbols: Tuple[str, ...] = ("*", "", "*")
	306	+ caption_position: str = "top"
	307	+ label_prefix: str = "tab:"
	308	+ font_size: str = "\\footnotesize"
	309	+ column_sep: str = " & "
	310	+ booktabs: bool = True
	311	+ threeparttable: bool = True
	312	+ note_width: str = "0.95\\linewidth"
	313	+
	314	+LATEX: LaTeXSettings = LaTeXSettings()
	315	+
	316	+# ---------------------------------------------------------------------------
	317	+# 12. Logging
	318	+# ---------------------------------------------------------------------------
	319	+
	320	+LOG_LEVEL: str = os.environ.get("HEDONIC_LOG_LEVEL", "INFO")
	321	+LOG_FORMAT: str = "%(asctime)s \| %(name)-20s \| %(levelname)-8s \| %(message)s"
	322	+LOG_DATE_FORMAT: str = "%Y-%m-%d %H:%M:%S"

added src/data_loader.py +549 −0

		`@@ -0,0 +1,549 @@`
	1	+"""
	2	+data_loader.py -- Data ingestion and validation for hedonic pricing analysis.
	3	+
	4	+Provides a uniform interface for loading the cleaned sample CSV, full
	5	+Inside Airbnb extracts (listings, calendar, reviews, neighbourhoods),
	6	+and any pre-processed interim artefacts. Every loader gracefully
	7	+handles missing files and returns ``None`` when data is unavailable.
	8	+"""
	9	+
	10	+from __future__ import annotations
	11	+
	12	+import logging
	13	+from pathlib import Path
	14	+from typing import Any, Dict, List, Optional, Tuple, Union
	15	+
	16	+import numpy as np
	17	+import pandas as pd
	18	+
	19	+from src.config import (
	20	+ CLEANED_SAMPLE_CSV,
	21	+ LISTINGS_CSV_GZ,
	22	+ CALENDAR_CSV_GZ,
	23	+ REVIEWS_CSV_GZ,
	24	+ NEIGHBOURHOODS_CSV,
	25	+ NEIGHBOURHOODS_GEOJSON,
	26	+ DATA_INTERIM,
	27	+ DATA_PROCESSED,
	28	+ QUEBEC_LAT_RANGE,
	29	+ QUEBEC_LON_RANGE,
	30	+ LOG_LEVEL,
	31	+ LOG_FORMAT,
	32	+ LOG_DATE_FORMAT,
	33	+)
	34	+
	35	+# ---------------------------------------------------------------------------
	36	+# Logger
	37	+# ---------------------------------------------------------------------------
	38	+
	39	+logging.basicConfig(format=LOG_FORMAT, datefmt=LOG_DATE_FORMAT, level=LOG_LEVEL)
	40	+logger = logging.getLogger(__name__)
	41	+
	42	+# ---------------------------------------------------------------------------
	43	+# Schema expectations (column name -> expected dtype family)
	44	+# ---------------------------------------------------------------------------
	45	+
	46	+_EXPECTED_COLUMNS_SAMPLE: Dict[str, str] = {
	47	+ "name": "object",
	48	+ "city": "object",
	49	+ "property_type": "object",
	50	+ "price_numeric": "float",
	51	+ "price_category": "object",
	52	+ "rating": "float",
	53	+ "rating_category": "object",
	54	+ "bedrooms": "int",
	55	+ "bathrooms": "float",
	56	+ "guests_count": "int",
	57	+ "amenities_count": "int",
	58	+ "num_images": "int",
	59	+ "num_reviews": "float",
	60	+ "quality_score": "float",
	61	+ "is_superhost": "bool",
	62	+ "is_guest_favorite": "bool",
	63	+ "pets_allowed": "bool",
	64	+ "lat": "float",
	65	+ "long": "float",
	66	+}
	67	+
	68	+# Columns that must be present for the pipeline to proceed
	69	+_REQUIRED_COLUMNS: List[str] = [
	70	+ "price_numeric", "bedrooms", "bathrooms", "guests_count",
	71	+ "lat", "long", "property_type",
	72	+]
	73	+
	74	+# ---------------------------------------------------------------------------
	75	+# Internal helpers
	76	+# ---------------------------------------------------------------------------
	77	+
	78	+
	79	+def _file_available(path: Path) -> bool:
	80	+ """Return True if path exists and is non-empty."""
	81	+ return path.is_file() and path.stat().st_size > 0
	82	+
	83	+
	84	+def _infer_separator(path: Path) -> str:
	85	+ """Sniff the first line to decide between comma and tab."""
	86	+ with open(path, "r", encoding="utf-8", errors="replace") as fh:
	87	+ first_line = fh.readline()
	88	+ if "\t" in first_line:
	89	+ return "\t"
	90	+ return ","
	91	+
	92	+
	93	+def _safe_read_csv(
	94	+ path: Path,
	95	+ *,
	96	+ compression: Optional[str] = None,
	97	+ nrows: Optional[int] = None,
	98	+ usecols: Optional[List[str]] = None,
	99	+ dtype: Optional[Dict[str, Any]] = None,
	100	+ low_memory: bool = False,
	101	+) -> Optional[pd.DataFrame]:
	102	+ """Read a CSV/TSV with graceful error handling.
	103	+
	104	+ Returns ``None`` when the file does not exist or cannot be parsed.
	105	+ """
	106	+ if not _file_available(path):
	107	+ logger.info("File not found or empty: %s", path)
	108	+ return None
	109	+
	110	+ sep = "," if compression else _infer_separator(path)
	111	+
	112	+ try:
	113	+ df = pd.read_csv(
	114	+ path,
	115	+ sep=sep,
	116	+ compression=compression or "infer",
	117	+ nrows=nrows,
	118	+ usecols=usecols,
	119	+ dtype=dtype,
	120	+ low_memory=low_memory,
	121	+ encoding="utf-8",
	122	+ on_bad_lines="warn",
	123	+ )
	124	+ logger.info(
	125	+ "Loaded %s rows x %s cols from %s",
	126	+ f"{len(df):,}",
	127	+ len(df.columns),
	128	+ path.name,
	129	+ )
	130	+ return df
	131	+ except Exception:
	132	+ logger.exception("Failed to read %s", path)
	133	+ return None
	134	+
	135	+
	136	+# ---------------------------------------------------------------------------
	137	+# Public loaders
	138	+# ---------------------------------------------------------------------------
	139	+
	140	+
	141	+def load_listings(
	142	+ *,
	143	+ path: Optional[Union[str, Path]] = None,
	144	+ nrows: Optional[int] = None,
	145	+ usecols: Optional[List[str]] = None,
	146	+) -> pd.DataFrame:
	147	+ """Load the primary listings dataset.
	148	+
	149	+ Resolution order:
	150	+ 1. Explicit path argument.
	151	+ 2. Full Inside Airbnb ``listings.csv.gz`` in ``data/raw/``.
	152	+ 3. ``cleaned_dataset_sample.csv`` at project root.
	153	+
	154	+ Raises
	155	+ ------
	156	+ FileNotFoundError
	157	+ If no listing data can be found at any location.
	158	+ """
	159	+ if path is not None:
	160	+ p = Path(path)
	161	+ df = _safe_read_csv(p, nrows=nrows, usecols=usecols)
	162	+ if df is not None:
	163	+ return df
	164	+ raise FileNotFoundError(f"Specified listing file not found: {p}")
	165	+
	166	+ # Try full extract first
	167	+ if _file_available(LISTINGS_CSV_GZ):
	168	+ df = _safe_read_csv(
	169	+ LISTINGS_CSV_GZ,
	170	+ compression="gzip",
	171	+ nrows=nrows,
	172	+ usecols=usecols,
	173	+ low_memory=True,
	174	+ )
	175	+ if df is not None:
	176	+ return df
	177	+
	178	+ # Fall back to cleaned sample
	179	+ if _file_available(CLEANED_SAMPLE_CSV):
	180	+ df = _safe_read_csv(CLEANED_SAMPLE_CSV, nrows=nrows, usecols=usecols)
	181	+ if df is not None:
	182	+ return df
	183	+
	184	+ raise FileNotFoundError(
	185	+ "No listing data found. Place listings.csv.gz in data/raw/ "
	186	+ "or ensure cleaned_dataset_sample.csv exists at the project root."
	187	+ )
	188	+
	189	+
	190	+def load_calendar(
	191	+ *,
	192	+ path: Optional[Union[str, Path]] = None,
	193	+ nrows: Optional[int] = None,
	194	+) -> Optional[pd.DataFrame]:
	195	+ """Load Inside Airbnb calendar data (availability & price over time).
	196	+
	197	+ Returns ``None`` if the file is not available.
	198	+ """
	199	+ p = Path(path) if path else CALENDAR_CSV_GZ
	200	+ compression = "gzip" if p.suffix == ".gz" else None
	201	+ df = _safe_read_csv(p, compression=compression, nrows=nrows, low_memory=True)
	202	+ if df is not None and "date" in df.columns:
	203	+ df["date"] = pd.to_datetime(df["date"], errors="coerce")
	204	+ return df
	205	+
	206	+
	207	+def load_reviews(
	208	+ *,
	209	+ path: Optional[Union[str, Path]] = None,
	210	+ nrows: Optional[int] = None,
	211	+) -> Optional[pd.DataFrame]:
	212	+ """Load Inside Airbnb reviews data.
	213	+
	214	+ Returns ``None`` if the file is not available.
	215	+ """
	216	+ p = Path(path) if path else REVIEWS_CSV_GZ
	217	+ compression = "gzip" if p.suffix == ".gz" else None
	218	+ df = _safe_read_csv(p, compression=compression, nrows=nrows, low_memory=True)
	219	+ if df is not None and "date" in df.columns:
	220	+ df["date"] = pd.to_datetime(df["date"], errors="coerce")
	221	+ return df
	222	+
	223	+
	224	+def load_neighbourhoods(
	225	+ *,
	226	+ path: Optional[Union[str, Path]] = None,
	227	+ prefer_geojson: bool = True,
	228	+) -> Optional[pd.DataFrame]:
	229	+ """Load neighbourhood boundaries or names.
	230	+
	231	+ If prefer_geojson is True (default), attempts GeoJSON first, then
	232	+ CSV. Returns a plain ``DataFrame`` (not GeoDataFrame) unless
	233	+ geopandas is installed, in which case it returns a ``GeoDataFrame``.
	234	+
	235	+ Returns ``None`` if no file is available.
	236	+ """
	237	+ csv_path = Path(path) if path else NEIGHBOURHOODS_CSV
	238	+ geojson_path = NEIGHBOURHOODS_GEOJSON
	239	+
	240	+ if prefer_geojson and _file_available(geojson_path):
	241	+ try:
	242	+ import geopandas as gpd # type: ignore[import-untyped]
	243	+
	244	+ gdf = gpd.read_file(geojson_path)
	245	+ logger.info(
	246	+ "Loaded %s neighbourhoods from GeoJSON", f"{len(gdf):,}"
	247	+ )
	248	+ return gdf
	249	+ except ImportError:
	250	+ logger.warning(
	251	+ "geopandas not installed; falling back to CSV neighbourhoods"
	252	+ )
	253	+ except Exception:
	254	+ logger.exception("Failed to read GeoJSON: %s", geojson_path)
	255	+
	256	+ if _file_available(csv_path):
	257	+ return _safe_read_csv(csv_path)
	258	+
	259	+ logger.info("No neighbourhood data available")
	260	+ return None
	261	+
	262	+
	263	+def load_interim(name: str) -> Optional[pd.DataFrame]:
	264	+ """Load a parquet or CSV from ``data/interim/`` by stem name."""
	265	+ parquet = DATA_INTERIM / f"{name}.parquet"
	266	+ csv = DATA_INTERIM / f"{name}.csv"
	267	+
	268	+ if _file_available(parquet):
	269	+ try:
	270	+ df = pd.read_parquet(parquet)
	271	+ logger.info("Loaded interim parquet: %s (%s rows)", name, f"{len(df):,}")
	272	+ return df
	273	+ except Exception:
	274	+ logger.exception("Failed to read interim parquet: %s", parquet)
	275	+
	276	+ if _file_available(csv):
	277	+ return _safe_read_csv(csv)
	278	+
	279	+ logger.info("Interim artefact '%s' not found", name)
	280	+ return None
	281	+
	282	+
	283	+def load_processed(name: str) -> Optional[pd.DataFrame]:
	284	+ """Load a parquet or CSV from ``data/processed/`` by stem name."""
	285	+ parquet = DATA_PROCESSED / f"{name}.parquet"
	286	+ csv = DATA_PROCESSED / f"{name}.csv"
	287	+
	288	+ if _file_available(parquet):
	289	+ try:
	290	+ df = pd.read_parquet(parquet)
	291	+ logger.info("Loaded processed parquet: %s (%s rows)", name, f"{len(df):,}")
	292	+ return df
	293	+ except Exception:
	294	+ logger.exception("Failed to read processed parquet: %s", parquet)
	295	+
	296	+ if _file_available(csv):
	297	+ return _safe_read_csv(csv)
	298	+
	299	+ logger.info("Processed artefact '%s' not found", name)
	300	+ return None
	301	+
	302	+
	303	+def load_all_data(
	304	+ *,
	305	+ nrows: Optional[int] = None,
	306	+) -> Dict[str, Optional[pd.DataFrame]]:
	307	+ """Master loader -- returns a dict of every available dataset.
	308	+
	309	+ Keys
	310	+ ----
	311	+ ``"listings"``
	312	+ Always present (raises if not).
	313	+ ``"calendar"``, ``"reviews"``, ``"neighbourhoods"``
	314	+ ``None`` when the corresponding raw file is missing.
	315	+
	316	+ Parameters
	317	+ ----------
	318	+ nrows : int, optional
	319	+ Limit rows for quick exploratory loads.
	320	+ """
	321	+ data: Dict[str, Optional[pd.DataFrame]] = {}
	322	+
	323	+ data["listings"] = load_listings(nrows=nrows)
	324	+ data["calendar"] = load_calendar(nrows=nrows)
	325	+ data["reviews"] = load_reviews(nrows=nrows)
	326	+ data["neighbourhoods"] = load_neighbourhoods()
	327	+
	328	+ available = [k for k, v in data.items() if v is not None]
	329	+ logger.info("Loaded datasets: %s", ", ".join(available))
	330	+ return data
	331	+
	332	+
	333	+# ---------------------------------------------------------------------------
	334	+# Validation
	335	+# ---------------------------------------------------------------------------
	336	+
	337	+
	338	+def validate_data(
	339	+ df: pd.DataFrame,
	340	+ *,
	341	+ strict: bool = False,
	342	+) -> Dict[str, Any]:
	343	+ """Validate a listings DataFrame against expected schema.
	344	+
	345	+ Parameters
	346	+ ----------
	347	+ df : pd.DataFrame
	348	+ The listings data to validate.
	349	+ strict : bool
	350	+ If True, raise ``ValueError`` on critical failures instead of
	351	+ just logging warnings.
	352	+
	353	+ Returns
	354	+ -------
	355	+ dict
	356	+ ``{"valid": bool, "warnings": [...], "errors": [...]}``.
	357	+ """
	358	+ warnings_list: List[str] = []
	359	+ errors_list: List[str] = []
	360	+
	361	+ # -- Required columns --------------------------------------------------
	362	+ missing_required = [c for c in _REQUIRED_COLUMNS if c not in df.columns]
	363	+ if missing_required:
	364	+ msg = f"Missing required columns: {missing_required}"
	365	+ errors_list.append(msg)
	366	+ logger.error(msg)
	367	+
	368	+ # -- Expected columns (informational) -----------------------------------
	369	+ missing_expected = [
	370	+ c for c in _EXPECTED_COLUMNS_SAMPLE if c not in df.columns
	371	+ ]
	372	+ if missing_expected:
	373	+ msg = f"Missing expected (non-critical) columns: {missing_expected}"
	374	+ warnings_list.append(msg)
	375	+ logger.warning(msg)
	376	+
	377	+ # -- Row count ----------------------------------------------------------
	378	+ if len(df) == 0:
	379	+ errors_list.append("DataFrame is empty")
	380	+ elif len(df) < 100:
	381	+ warnings_list.append(
	382	+ f"Very small dataset ({len(df)} rows); results may be unreliable"
	383	+ )
	384	+
	385	+ # -- Price range --------------------------------------------------------
	386	+ if "price_numeric" in df.columns:
	387	+ prices = df["price_numeric"].dropna()
	388	+ if len(prices) == 0:
	389	+ errors_list.append("All price values are missing")
	390	+ else:
	391	+ if prices.min() <= 0:
	392	+ warnings_list.append(
	393	+ f"Prices include non-positive values (min={prices.min():.2f})"
	394	+ )
	395	+ if prices.max() > 100_000:
	396	+ warnings_list.append(
	397	+ f"Extreme price outliers present (max={prices.max():,.0f})"
	398	+ )
	399	+
	400	+ # -- Coordinate ranges --------------------------------------------------
	401	+ if "lat" in df.columns and "long" in df.columns:
	402	+ lat_min, lat_max = QUEBEC_LAT_RANGE
	403	+ lon_min, lon_max = QUEBEC_LON_RANGE
	404	+
	405	+ out_of_bounds = df[
	406	+ (df["lat"] < lat_min)
	407	+ \| (df["lat"] > lat_max)
	408	+ \| (df["long"] < lon_min)
	409	+ \| (df["long"] > lon_max)
	410	+ ]
	411	+ if len(out_of_bounds) > 0:
	412	+ pct = 100.0 * len(out_of_bounds) / len(df)
	413	+ warnings_list.append(
	414	+ f"{len(out_of_bounds)} listings ({pct:.1f}%) have coordinates "
	415	+ f"outside Quebec bounding box"
	416	+ )
	417	+
	418	+ # -- Duplicate coordinates (exact) --------------------------------------
	419	+ if "lat" in df.columns and "long" in df.columns:
	420	+ dup_coords = df.duplicated(subset=["lat", "long"], keep=False).sum()
	421	+ if dup_coords > 0:
	422	+ pct = 100.0 * dup_coords / len(df)
	423	+ warnings_list.append(
	424	+ f"{dup_coords} listings ({pct:.1f}%) share exact coordinates "
	425	+ f"(possible duplicates or jittered points)"
	426	+ )
	427	+
	428	+ # -- Boolean columns ----------------------------------------------------
	429	+ for col in ("is_superhost", "is_guest_favorite", "pets_allowed"):
	430	+ if col in df.columns and df[col].dtype not in ("bool", "boolean"):
	431	+ warnings_list.append(
	432	+ f"Column '{col}' has dtype {df[col].dtype}, expected bool"
	433	+ )
	434	+
	435	+ # -- Rating range -------------------------------------------------------
	436	+ if "rating" in df.columns:
	437	+ valid_ratings = df["rating"].dropna()
	438	+ if len(valid_ratings) > 0:
	439	+ if valid_ratings.min() < 0 or valid_ratings.max() > 5:
	440	+ warnings_list.append(
	441	+ f"Ratings outside 0-5 range: "
	442	+ f"min={valid_ratings.min():.2f}, max={valid_ratings.max():.2f}"
	443	+ )
	444	+
	445	+ # -- Summary ------------------------------------------------------------
	446	+ is_valid = len(errors_list) == 0
	447	+ result = {
	448	+ "valid": is_valid,
	449	+ "warnings": warnings_list,
	450	+ "errors": errors_list,
	451	+ "n_rows": len(df),
	452	+ "n_cols": len(df.columns),
	453	+ "columns": list(df.columns),
	454	+ }
	455	+
	456	+ if is_valid:
	457	+ logger.info(
	458	+ "Validation passed (%s warnings)", len(warnings_list)
	459	+ )
	460	+ else:
	461	+ logger.error("Validation FAILED with %s error(s)", len(errors_list))
	462	+
	463	+ if strict and not is_valid:
	464	+ raise ValueError(
	465	+ "Data validation failed:\n" + "\n".join(errors_list)
	466	+ )
	467	+
	468	+ return result
	469	+
	470	+
	471	+# ---------------------------------------------------------------------------
	472	+# Summary statistics
	473	+# ---------------------------------------------------------------------------
	474	+
	475	+
	476	+def get_data_summary(df: pd.DataFrame) -> Dict[str, Any]:
	477	+ """Return a rich summary-statistics dictionary for a listings DataFrame.
	478	+
	479	+ Useful for dashboards, logging, and the cleaning report.
	480	+ """
	481	+ summary: Dict[str, Any] = {
	482	+ "n_rows": len(df),
	483	+ "n_cols": len(df.columns),
	484	+ "columns": list(df.columns),
	485	+ "dtypes": {col: str(dt) for col, dt in df.dtypes.items()},
	486	+ "missing_counts": df.isnull().sum().to_dict(),
	487	+ "missing_pct": (100.0 * df.isnull().mean()).round(2).to_dict(),
	488	+ }
	489	+
	490	+ # Numeric column stats
	491	+ numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
	492	+ if numeric_cols:
	493	+ desc = df[numeric_cols].describe(percentiles=[0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99])
	494	+ summary["numeric_stats"] = desc.to_dict()
	495	+
	496	+ # Price specifics
	497	+ if "price_numeric" in df.columns:
	498	+ prices = df["price_numeric"].dropna()
	499	+ summary["price"] = {
	500	+ "count": int(len(prices)),
	501	+ "missing": int(df["price_numeric"].isnull().sum()),
	502	+ "mean": float(prices.mean()) if len(prices) > 0 else None,
	503	+ "median": float(prices.median()) if len(prices) > 0 else None,
	504	+ "std": float(prices.std()) if len(prices) > 0 else None,
	505	+ "min": float(prices.min()) if len(prices) > 0 else None,
	506	+ "max": float(prices.max()) if len(prices) > 0 else None,
	507	+ "p1": float(prices.quantile(0.01)) if len(prices) > 0 else None,
	508	+ "p5": float(prices.quantile(0.05)) if len(prices) > 0 else None,
	509	+ "p95": float(prices.quantile(0.95)) if len(prices) > 0 else None,
	510	+ "p99": float(prices.quantile(0.99)) if len(prices) > 0 else None,
	511	+ "skewness": float(prices.skew()) if len(prices) > 0 else None,
	512	+ "kurtosis": float(prices.kurtosis()) if len(prices) > 0 else None,
	513	+ }
	514	+
	515	+ # Categorical breakdowns
	516	+ for cat_col in ("city", "property_type", "price_category", "rating_category", "room_type"):
	517	+ if cat_col in df.columns:
	518	+ vc = df[cat_col].value_counts(dropna=False)
	519	+ summary[f"{cat_col}_counts"] = vc.to_dict()
	520	+
	521	+ # Boolean flags
	522	+ for bool_col in ("is_superhost", "is_guest_favorite", "pets_allowed"):
	523	+ if bool_col in df.columns:
	524	+ true_count = int(df[bool_col].sum())
	525	+ summary[f"{bool_col}_rate"] = round(
	526	+ 100.0 * true_count / len(df), 2
	527	+ ) if len(df) > 0 else 0.0
	528	+
	529	+ # Geo extent
	530	+ if "lat" in df.columns and "long" in df.columns:
	531	+ summary["geo_extent"] = {
	532	+ "lat_min": float(df["lat"].min()),
	533	+ "lat_max": float(df["lat"].max()),
	534	+ "lon_min": float(df["long"].min()),
	535	+ "lon_max": float(df["long"].max()),
	536	+ }
	537	+
	538	+ # Top cities by count
	539	+ if "city" in df.columns:
	540	+ top_cities = df["city"].value_counts().head(15)
	541	+ summary["top_cities"] = top_cities.to_dict()
	542	+
	543	+ logger.info(
	544	+ "Data summary: %s rows, %s cols, %s numeric features",
	545	+ f"{summary['n_rows']:,}",
	546	+ summary["n_cols"],
	547	+ len(numeric_cols),
	548	+ )
	549	+ return summary

added src/feature_engineering.py +240 −0

		`@@ -0,0 +1,240 @@`
	1	+"""Master feature engineering for hedonic pricing analysis of Airbnb listings."""
	2	+
	3	+from __future__ import annotations
	4	+
	5	+import logging
	6	+from typing import Any
	7	+
	8	+import numpy as np
	9	+import pandas as pd
	10	+
	11	+logger = logging.getLogger(__name__)
	12	+
	13	+# ---------------------------------------------------------------------------
	14	+# Structural features
	15	+# ---------------------------------------------------------------------------
	16	+
	17	+def create_structural_features(df: pd.DataFrame) -> pd.DataFrame:
	18	+ """Derive structural / capacity features from bedrooms, bathrooms, guests_count, property_type."""
	19	+ out = df.copy()
	20	+
	21	+ # Log transforms (add 1 to avoid log(0))
	22	+ out["log_accommodates"] = np.log1p(out["guests_count"])
	23	+ out["log_bedrooms"] = np.log1p(out["bedrooms"])
	24	+ out["log_beds"] = np.log1p(out["bedrooms"]) # beds not available; proxy with bedrooms
	25	+ out["log_bathrooms"] = np.log1p(out["bathrooms"])
	26	+
	27	+ # Per-guest ratios (guard against division by zero)
	28	+ guests_safe = out["guests_count"].replace(0, np.nan)
	29	+ out["bathrooms_per_guest"] = out["bathrooms"] / guests_safe
	30	+ out["bedrooms_per_guest"] = out["bedrooms"] / guests_safe
	31	+ out["bathrooms_per_guest"] = out["bathrooms_per_guest"].fillna(0.0)
	32	+ out["bedrooms_per_guest"] = out["bedrooms_per_guest"].fillna(0.0)
	33	+
	34	+ # Property-type dummies
	35	+ pt_lower = out["property_type"].str.lower().fillna("")
	36	+ out["is_entire_home"] = (
	37	+ pt_lower.str.contains("house")
	38	+ \| pt_lower.str.contains("condo")
	39	+ \| pt_lower.str.contains("apartment")
	40	+ \| pt_lower.str.contains("cabin")
	41	+ \| pt_lower.str.contains("chalet")
	42	+ ).astype(int)
	43	+ out["is_private_room"] = pt_lower.str.contains("rental unit").astype(int)
	44	+ out["is_shared_room"] = pt_lower.str.contains("other").astype(int)
	45	+
	46	+ # Capacity flag
	47	+ out["large_capacity_listing"] = (out["guests_count"] >= 8).astype(int)
	48	+
	49	+ # Polynomial terms
	50	+ out["accommodates_sq"] = out["guests_count"] ** 2
	51	+ out["bedrooms_sq"] = out["bedrooms"] ** 2
	52	+ out["bathrooms_sq"] = out["bathrooms"] ** 2
	53	+ out["accommodates_bedrooms"] = out["guests_count"] * out["bedrooms"]
	54	+ out["accommodates_bathrooms"] = out["guests_count"] * out["bathrooms"]
	55	+
	56	+ logger.info("Structural features created: %d new columns", out.shape[1] - df.shape[1])
	57	+ return out
	58	+
	59	+
	60	+# ---------------------------------------------------------------------------
	61	+# Host features
	62	+# ---------------------------------------------------------------------------
	63	+
	64	+def create_host_features(df: pd.DataFrame) -> pd.DataFrame:
	65	+ """Derive host-quality features."""
	66	+ out = df.copy()
	67	+
	68	+ # is_superhost already boolean; cast to int for modelling
	69	+ out["superhost_flag"] = out["is_superhost"].astype(int)
	70	+
	71	+ # host_professional_dummy: not available in limited data
	72	+ out["host_professional_dummy"] = 0
	73	+
	74	+ # multi_listing_host: proxy via name frequency (same host likely uses similar names)
	75	+ name_counts = out["name"].map(out["name"].value_counts())
	76	+ out["multi_listing_host"] = (name_counts > 1).astype(int)
	77	+
	78	+ # Interaction: superhost x entire home (requires structural features already present)
	79	+ if "is_entire_home" in out.columns:
	80	+ out["superhost_x_entire_home"] = out["superhost_flag"] * out["is_entire_home"]
	81	+ else:
	82	+ pt_lower = out["property_type"].str.lower().fillna("")
	83	+ is_entire = (
	84	+ pt_lower.str.contains("house")
	85	+ \| pt_lower.str.contains("condo")
	86	+ \| pt_lower.str.contains("apartment")
	87	+ \| pt_lower.str.contains("cabin")
	88	+ \| pt_lower.str.contains("chalet")
	89	+ ).astype(int)
	90	+ out["superhost_x_entire_home"] = out["superhost_flag"] * is_entire
	91	+
	92	+ # Guest favourite flag
	93	+ out["guest_favorite_flag"] = out["is_guest_favorite"].astype(int)
	94	+
	95	+ logger.info("Host features created: %d new columns", out.shape[1] - df.shape[1])
	96	+ return out
	97	+
	98	+
	99	+# ---------------------------------------------------------------------------
	100	+# Review / reputation features
	101	+# ---------------------------------------------------------------------------
	102	+
	103	+def create_review_features(df: pd.DataFrame) -> pd.DataFrame:
	104	+ """Derive review and reputation features from rating and num_reviews."""
	105	+ out = df.copy()
	106	+
	107	+ # Log reviews (handle NaN)
	108	+ out["log_reviews"] = np.log1p(out["num_reviews"].fillna(0))
	109	+
	110	+ # Review quality bucket from rating
	111	+ rating_filled = out["rating"].fillna(0.0)
	112	+ out["review_quality"] = pd.cut(
	113	+ rating_filled,
	114	+ bins=[-np.inf, 0.0, 4.0, 4.5, 4.8, 5.01],
	115	+ labels=[0, 1, 2, 3, 4],
	116	+ ).astype(float)
	117	+
	118	+ # Reputation score: weighted combination of rating and log(reviews)
	119	+ rating_norm = rating_filled / 5.0
	120	+ review_norm = out["log_reviews"] / (out["log_reviews"].max() or 1.0)
	121	+ out["reputation_score"] = 0.6 * rating_norm + 0.4 * review_norm
	122	+
	123	+ # Cleanliness premium proxy: high rating (>= 4.8) with many reviews signals cleanliness
	124	+ out["cleanliness_premium"] = (
	125	+ (rating_filled >= 4.8) & (out["num_reviews"].fillna(0) >= 10)
	126	+ ).astype(int)
	127	+
	128	+ # Location rating premium proxy: high rating in a high-density area (use quality_score if helpful)
	129	+ out["location_rating_premium"] = (
	130	+ (rating_filled >= 4.7) & (out["quality_score"] >= out["quality_score"].median())
	131	+ ).astype(int)
	132	+
	133	+ # Has reviews flag
	134	+ out["has_reviews"] = (out["num_reviews"].fillna(0) > 0).astype(int)
	135	+
	136	+ # No rating flag (for imputation awareness)
	137	+ out["no_rating_flag"] = out["rating"].isna().astype(int)
	138	+
	139	+ logger.info("Review features created: %d new columns", out.shape[1] - df.shape[1])
	140	+ return out
	141	+
	142	+
	143	+# ---------------------------------------------------------------------------
	144	+# Price segment features
	145	+# ---------------------------------------------------------------------------
	146	+
	147	+def create_price_segment_features(df: pd.DataFrame) -> pd.DataFrame:
	148	+ """Categorize listings into price segments based on quantiles."""
	149	+ out = df.copy()
	150	+
	151	+ price = out["price_numeric"]
	152	+ valid_mask = price.notna()
	153	+
	154	+ # Quantile boundaries on valid prices
	155	+ q25, q50, q75 = price.quantile([0.25, 0.50, 0.75])
	156	+
	157	+ conditions = [
	158	+ price <= q25,
	159	+ (price > q25) & (price <= q50),
	160	+ (price > q50) & (price <= q75),
	161	+ price > q75,
	162	+ ]
	163	+ choices = ["budget", "moderate", "premium", "luxury"]
	164	+ out["price_segment"] = np.select(conditions, choices, default="unknown")
	165	+
	166	+ # Numeric encoding
	167	+ segment_map = {"budget": 0, "moderate": 1, "premium": 2, "luxury": 3, "unknown": -1}
	168	+ out["price_segment_code"] = out["price_segment"].map(segment_map).astype(int)
	169	+
	170	+ # Log price (primary dependent variable in hedonic models)
	171	+ out["log_price"] = np.log1p(price)
	172	+
	173	+ # Price per guest
	174	+ guests_safe = out["guests_count"].replace(0, np.nan)
	175	+ out["price_per_guest"] = price / guests_safe
	176	+
	177	+ # Price per bedroom
	178	+ bedrooms_safe = out["bedrooms"].replace(0, np.nan)
	179	+ out["price_per_bedroom"] = price / bedrooms_safe
	180	+
	181	+ logger.info("Price segment features created: %d new columns", out.shape[1] - df.shape[1])
	182	+ return out
	183	+
	184	+
	185	+# ---------------------------------------------------------------------------
	186	+# Master pipeline
	187	+# ---------------------------------------------------------------------------
	188	+
	189	+def create_all_features(df: pd.DataFrame) -> pd.DataFrame:
	190	+ """Run the full feature engineering pipeline in order."""
	191	+ logger.info("Starting feature engineering pipeline on %d rows", len(df))
	192	+ out = df.copy()
	193	+ out = create_structural_features(out)
	194	+ out = create_host_features(out)
	195	+ out = create_review_features(out)
	196	+ out = create_price_segment_features(out)
	197	+ logger.info("Pipeline complete: %d columns total", out.shape[1])
	198	+ return out
	199	+
	200	+
	201	+# ---------------------------------------------------------------------------
	202	+# Feature group registry
	203	+# ---------------------------------------------------------------------------
	204	+
	205	+def get_feature_groups() -> dict[str, list[str]]:
	206	+ """Return a dict mapping group names to lists of feature column names."""
	207	+ return {
	208	+ "structural": [
	209	+ "log_accommodates", "log_bedrooms", "log_beds", "log_bathrooms",
	210	+ "bathrooms_per_guest", "bedrooms_per_guest",
	211	+ "is_entire_home", "is_private_room", "is_shared_room",
	212	+ "large_capacity_listing",
	213	+ "accommodates_sq", "bedrooms_sq", "bathrooms_sq",
	214	+ "accommodates_bedrooms", "accommodates_bathrooms",
	215	+ ],
	216	+ "host": [
	217	+ "superhost_flag", "host_professional_dummy",
	218	+ "multi_listing_host", "superhost_x_entire_home",
	219	+ "guest_favorite_flag",
	220	+ ],
	221	+ "review": [
	222	+ "log_reviews", "review_quality", "reputation_score",
	223	+ "cleanliness_premium", "location_rating_premium",
	224	+ "has_reviews", "no_rating_flag",
	225	+ ],
	226	+ "price_segment": [
	227	+ "price_segment", "price_segment_code",
	228	+ "log_price", "price_per_guest", "price_per_bedroom",
	229	+ ],
	230	+ "amenity": [
	231	+ "amenity_richness_index", "luxury_index",
	232	+ "business_travel_index", "family_comfort_index",
	233	+ "leisure_index", "remote_work_index",
	234	+ ],
	235	+ "spatial": [
	236	+ "distance_to_center", "local_density_500m", "local_density_1km",
	237	+ "local_avg_price_500m", "local_avg_price_1km",
	238	+ "spatial_cluster",
	239	+ ],
	240	+ }

added src/figures.py +749 −0

		`@@ -0,0 +1,749 @@`
	1	+"""
	2	+figures.py - Publication-quality figure generation for hedonic pricing analysis.
	3	+
	4	+All figures use matplotlib + seaborn with a consistent 'whitegrid' style,
	5	+(10, 6) default size, 300 DPI, and are saved as both PNG and PDF for
	6	+LaTeX / journal submission compatibility.
	7	+
	8	+Author: Simon-Pierre Boucher
	9	+Date: 2026-05-20
	10	+"""
	11	+
	12	+from __future__ import annotations
	13	+
	14	+import logging
	15	+import os
	16	+from pathlib import Path
	17	+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
	18	+
	19	+import matplotlib.pyplot as plt
	20	+import matplotlib.ticker as mticker
	21	+import numpy as np
	22	+import pandas as pd
	23	+import seaborn as sns
	24	+
	25	+logger = logging.getLogger(__name__)
	26	+
	27	+# ---------------------------------------------------------------------------
	28	+# Global style defaults
	29	+# ---------------------------------------------------------------------------
	30	+
	31	+_DEFAULT_FIGSIZE: Tuple[int, int] = (10, 6)
	32	+_DEFAULT_DPI: int = 300
	33	+_STYLE: str = "whitegrid"
	34	+_PALETTE: str = "muted"
	35	+
	36	+# Publication-quality font sizes
	37	+_RC_PARAMS: Dict[str, Any] = {
	38	+ "font.size": 11,
	39	+ "axes.titlesize": 13,
	40	+ "axes.labelsize": 12,
	41	+ "xtick.labelsize": 10,
	42	+ "ytick.labelsize": 10,
	43	+ "legend.fontsize": 10,
	44	+ "figure.titlesize": 14,
	45	+ "font.family": "serif",
	46	+}
	47	+
	48	+
	49	+def _apply_style() -> None:
	50	+ """Apply consistent seaborn + matplotlib style."""
	51	+ sns.set_style(_STYLE)
	52	+ sns.set_palette(_PALETTE)
	53	+ plt.rcParams.update(_RC_PARAMS)
	54	+
	55	+
	56	+def _save_figure(
	57	+ fig: plt.Figure,
	58	+ filename: str,
	59	+ save_dir: str,
	60	+) -> List[str]:
	61	+ """Save a figure as PNG and PDF, return list of saved paths."""
	62	+ os.makedirs(save_dir, exist_ok=True)
	63	+ paths: List[str] = []
	64	+ for ext in ("png", "pdf"):
	65	+ stem = Path(filename).stem
	66	+ fpath = os.path.join(save_dir, f"{stem}.{ext}")
	67	+ fig.savefig(fpath, dpi=_DEFAULT_DPI, bbox_inches="tight",
	68	+ facecolor="white", edgecolor="none")
	69	+ paths.append(fpath)
	70	+ logger.info("Saved figure: %s", fpath)
	71	+ plt.close(fig)
	72	+ return paths
	73	+
	74	+
	75	+# ---------------------------------------------------------------------------
	76	+# Figure 01 - Price distribution
	77	+# ---------------------------------------------------------------------------
	78	+
	79	+def plot_price_distribution(
	80	+ df: pd.DataFrame,
	81	+ save_dir: str,
	82	+ price_col: str = "price_numeric",
	83	+ figsize: Tuple[int, int] = _DEFAULT_FIGSIZE,
	84	+) -> List[str]:
	85	+ """
	86	+ 01_price_distribution.png -- Histogram + KDE of raw prices.
	87	+
	88	+ Parameters
	89	+ ----------
	90	+ df : pd.DataFrame
	91	+ save_dir : str
	92	+ price_col : str
	93	+ figsize : tuple
	94	+
	95	+ Returns
	96	+ -------
	97	+ list[str]
	98	+ Paths to saved files.
	99	+ """
	100	+ _apply_style()
	101	+ prices = df[price_col].dropna()
	102	+
	103	+ fig, ax = plt.subplots(figsize=figsize)
	104	+ sns.histplot(prices, bins=80, kde=True, color="#4C72B0",
	105	+ edgecolor="white", linewidth=0.5, ax=ax)
	106	+ ax.set_xlabel("Nightly Price (CAD)")
	107	+ ax.set_ylabel("Frequency")
	108	+ ax.set_title("Distribution of Airbnb Listing Prices")
	109	+
	110	+ # Annotate key statistics
	111	+ med = prices.median()
	112	+ mean = prices.mean()
	113	+ ax.axvline(med, color="#C44E52", linestyle="--", linewidth=1.2,
	114	+ label=f"Median = ${med:,.0f}")
	115	+ ax.axvline(mean, color="#DD8452", linestyle="--", linewidth=1.2,
	116	+ label=f"Mean = ${mean:,.0f}")
	117	+ ax.legend(frameon=True, fancybox=False, edgecolor="0.8")
	118	+
	119	+ ax.xaxis.set_major_formatter(mticker.StrMethodFormatter("${x:,.0f}"))
	120	+ fig.tight_layout()
	121	+ return _save_figure(fig, "01_price_distribution", save_dir)
	122	+
	123	+
	124	+# ---------------------------------------------------------------------------
	125	+# Figure 02 - Log-price distribution
	126	+# ---------------------------------------------------------------------------
	127	+
	128	+def plot_log_price_distribution(
	129	+ df: pd.DataFrame,
	130	+ save_dir: str,
	131	+ price_col: str = "price_numeric",
	132	+ figsize: Tuple[int, int] = _DEFAULT_FIGSIZE,
	133	+) -> List[str]:
	134	+ """02_log_price_distribution.png -- Histogram + KDE of ln(price)."""
	135	+ _apply_style()
	136	+ prices = df[price_col].dropna()
	137	+ log_prices = np.log(prices[prices > 0])
	138	+
	139	+ fig, ax = plt.subplots(figsize=figsize)
	140	+ sns.histplot(log_prices, bins=60, kde=True, color="#55A868",
	141	+ edgecolor="white", linewidth=0.5, ax=ax)
	142	+ ax.set_xlabel("ln(Nightly Price)")
	143	+ ax.set_ylabel("Frequency")
	144	+ ax.set_title("Distribution of Log-Transformed Prices\n"
	145	+ "(Semi-log hedonic model dependent variable)")
	146	+
	147	+ med = log_prices.median()
	148	+ ax.axvline(med, color="#C44E52", linestyle="--", linewidth=1.2,
	149	+ label=f"Median ln(price) = {med:.2f}")
	150	+ ax.legend(frameon=True, fancybox=False, edgecolor="0.8")
	151	+
	152	+ # Shapiro-Wilk note (sample if too large)
	153	+ from scipy import stats as sp_stats
	154	+ sample = log_prices.sample(min(5000, len(log_prices)), random_state=42)
	155	+ _, sw_p = sp_stats.shapiro(sample)
	156	+ ax.annotate(f"Shapiro-Wilk p = {sw_p:.4f} (n={len(sample)})",
	157	+ xy=(0.98, 0.95), xycoords="axes fraction",
	158	+ ha="right", va="top", fontsize=9,
	159	+ bbox=dict(boxstyle="round,pad=0.3", fc="white",
	160	+ ec="0.8", alpha=0.9))
	161	+
	162	+ fig.tight_layout()
	163	+ return _save_figure(fig, "02_log_price_distribution", save_dir)
	164	+
	165	+
	166	+# ---------------------------------------------------------------------------
	167	+# Figure 03 - Price map
	168	+# ---------------------------------------------------------------------------
	169	+
	170	+def plot_price_map(
	171	+ df: pd.DataFrame,
	172	+ save_dir: str,
	173	+ price_col: str = "price_numeric",
	174	+ lat_col: str = "lat",
	175	+ lon_col: str = "long",
	176	+ figsize: Tuple[int, int] = (12, 10),
	177	+) -> List[str]:
	178	+ """03_price_map.png -- Scatter on lat/long coloured by price."""
	179	+ _apply_style()
	180	+ sub = df.dropna(subset=[price_col, lat_col, lon_col]).copy()
	181	+
	182	+ # Cap at 99th percentile for colour scale
	183	+ p99 = sub[price_col].quantile(0.99)
	184	+ sub["price_capped"] = sub[price_col].clip(upper=p99)
	185	+
	186	+ fig, ax = plt.subplots(figsize=figsize)
	187	+ scatter = ax.scatter(
	188	+ sub[lon_col], sub[lat_col],
	189	+ c=sub["price_capped"], cmap="RdYlGn_r",
	190	+ s=8, alpha=0.6, edgecolors="none",
	191	+ )
	192	+ cbar = fig.colorbar(scatter, ax=ax, pad=0.02, shrink=0.8)
	193	+ cbar.set_label("Nightly Price (CAD)", fontsize=11)
	194	+
	195	+ ax.set_xlabel("Longitude")
	196	+ ax.set_ylabel("Latitude")
	197	+ ax.set_title("Spatial Distribution of Airbnb Prices -- Quebec")
	198	+
	199	+ fig.tight_layout()
	200	+ return _save_figure(fig, "03_price_map", save_dir)
	201	+
	202	+
	203	+# ---------------------------------------------------------------------------
	204	+# Figure 04 - Price by neighbourhood / city
	205	+# ---------------------------------------------------------------------------
	206	+
	207	+def plot_price_by_neighbourhood(
	208	+ df: pd.DataFrame,
	209	+ save_dir: str,
	210	+ price_col: str = "price_numeric",
	211	+ geo_col: str = "city",
	212	+ top_n: int = 20,
	213	+ figsize: Tuple[int, int] = (12, 7),
	214	+) -> List[str]:
	215	+ """04_price_by_neighbourhood.png -- Top 20 cities/neighbourhoods."""
	216	+ _apply_style()
	217	+ sub = df.dropna(subset=[price_col, geo_col]).copy()
	218	+
	219	+ # Top N by listing count
	220	+ top_areas = (
	221	+ sub[geo_col].value_counts().head(top_n).index.tolist()
	222	+ )
	223	+ sub = sub[sub[geo_col].isin(top_areas)]
	224	+
	225	+ # Order by median price
	226	+ order = (
	227	+ sub.groupby(geo_col, observed=True)[price_col]
	228	+ .median().sort_values(ascending=False).index.tolist()
	229	+ )
	230	+
	231	+ fig, ax = plt.subplots(figsize=figsize)
	232	+ sns.boxplot(
	233	+ data=sub, y=geo_col, x=price_col, order=order,
	234	+ palette="coolwarm", showfliers=False, ax=ax,
	235	+ linewidth=0.8,
	236	+ )
	237	+ ax.set_xlabel("Nightly Price (CAD)")
	238	+ ax.set_ylabel("")
	239	+ ax.set_title(f"Price Distribution by Top {top_n} Cities / Neighbourhoods")
	240	+ ax.xaxis.set_major_formatter(mticker.StrMethodFormatter("${x:,.0f}"))
	241	+
	242	+ fig.tight_layout()
	243	+ return _save_figure(fig, "04_price_by_neighbourhood", save_dir)
	244	+
	245	+
	246	+# ---------------------------------------------------------------------------
	247	+# Figure 05 - Price by room type
	248	+# ---------------------------------------------------------------------------
	249	+
	250	+def plot_price_by_room_type(
	251	+ df: pd.DataFrame,
	252	+ save_dir: str,
	253	+ price_col: str = "price_numeric",
	254	+ room_col: str = "property_type",
	255	+ figsize: Tuple[int, int] = _DEFAULT_FIGSIZE,
	256	+) -> List[str]:
	257	+ """05_price_by_room_type.png -- Violin + strip plot by room/property type."""
	258	+ _apply_style()
	259	+ sub = df.dropna(subset=[price_col, room_col]).copy()
	260	+
	261	+ # Keep types with enough observations
	262	+ counts = sub[room_col].value_counts()
	263	+ valid_types = counts[counts >= 10].index.tolist()
	264	+ sub = sub[sub[room_col].isin(valid_types)]
	265	+
	266	+ order = (
	267	+ sub.groupby(room_col, observed=True)[price_col]
	268	+ .median().sort_values(ascending=False).index.tolist()
	269	+ )
	270	+
	271	+ fig, ax = plt.subplots(figsize=figsize)
	272	+ sns.violinplot(
	273	+ data=sub, y=room_col, x=price_col, order=order,
	274	+ palette="Set2", inner="quartile", cut=0, ax=ax,
	275	+ linewidth=0.8,
	276	+ )
	277	+ ax.set_xlabel("Nightly Price (CAD)")
	278	+ ax.set_ylabel("")
	279	+ ax.set_title("Price Distribution by Property Type")
	280	+ ax.xaxis.set_major_formatter(mticker.StrMethodFormatter("${x:,.0f}"))
	281	+
	282	+ fig.tight_layout()
	283	+ return _save_figure(fig, "05_price_by_room_type", save_dir)
	284	+
	285	+
	286	+# ---------------------------------------------------------------------------
	287	+# Figure 06 - Amenity premiums
	288	+# ---------------------------------------------------------------------------
	289	+
	290	+def plot_amenity_premiums(
	291	+ premiums_df: pd.DataFrame,
	292	+ save_dir: str,
	293	+ top_n: int = 20,
	294	+ figsize: Tuple[int, int] = (10, 8),
	295	+) -> List[str]:
	296	+ """06_amenity_premiums.png -- Horizontal bar chart of % premiums."""
	297	+ _apply_style()
	298	+ df = premiums_df.copy()
	299	+
	300	+ # Normalise column names
	301	+ if "% Premium" not in df.columns:
	302	+ for c in df.columns:
	303	+ if "premium" in c.lower() or "effect" in c.lower():
	304	+ df = df.rename(columns={c: "% Premium"})
	305	+ break
	306	+ if "Amenity" not in df.columns:
	307	+ for c in df.columns:
	308	+ if "variable" in c.lower() or "amenity" in c.lower() or "feature" in c.lower():
	309	+ df = df.rename(columns={c: "Amenity"})
	310	+ break
	311	+
	312	+ df = df.sort_values("% Premium", ascending=True).tail(top_n)
	313	+
	314	+ fig, ax = plt.subplots(figsize=figsize)
	315	+ colours = ["#C44E52" if v < 0 else "#55A868" for v in df["% Premium"]]
	316	+ ax.barh(df["Amenity"], df["% Premium"], color=colours, edgecolor="white",
	317	+ linewidth=0.5)
	318	+ ax.set_xlabel("Price Premium (%)")
	319	+ ax.set_ylabel("")
	320	+ ax.set_title("Estimated Amenity Price Premiums\n(from semi-log hedonic model)")
	321	+ ax.axvline(0, color="black", linewidth=0.8)
	322	+
	323	+ # Value labels
	324	+ for i, (val, name) in enumerate(zip(df["% Premium"], df["Amenity"])):
	325	+ ax.text(val + (0.3 if val >= 0 else -0.3), i,
	326	+ f"{val:+.1f}%", va="center",
	327	+ ha="left" if val >= 0 else "right", fontsize=9)
	328	+
	329	+ fig.tight_layout()
	330	+ return _save_figure(fig, "06_amenity_premiums", save_dir)
	331	+
	332	+
	333	+# ---------------------------------------------------------------------------
	334	+# Figure 07 - Review score premium
	335	+# ---------------------------------------------------------------------------
	336	+
	337	+def plot_review_score_premium(
	338	+ df: pd.DataFrame,
	339	+ save_dir: str,
	340	+ price_col: str = "price_numeric",
	341	+ rating_col: str = "rating",
	342	+ figsize: Tuple[int, int] = _DEFAULT_FIGSIZE,
	343	+) -> List[str]:
	344	+ """07_review_score_premium.png -- Price vs. review score."""
	345	+ _apply_style()
	346	+ sub = df.dropna(subset=[price_col, rating_col]).copy()
	347	+ sub = sub[sub[price_col] > 0]
	348	+
	349	+ fig, axes = plt.subplots(1, 2, figsize=(14, 6))
	350	+
	351	+ # Left: scatter + regression line
	352	+ ax = axes[0]
	353	+ sns.regplot(
	354	+ data=sub, x=rating_col, y=price_col,
	355	+ scatter_kws={"alpha": 0.15, "s": 10, "color": "#4C72B0"},
	356	+ line_kws={"color": "#C44E52", "linewidth": 1.5},
	357	+ ax=ax, ci=95,
	358	+ )
	359	+ ax.set_xlabel("Review Rating")
	360	+ ax.set_ylabel("Nightly Price (CAD)")
	361	+ ax.set_title("Price vs. Review Score")
	362	+ ax.yaxis.set_major_formatter(mticker.StrMethodFormatter("${x:,.0f}"))
	363	+
	364	+ # Right: binned means with error bars
	365	+ ax = axes[1]
	366	+ sub["rating_bin"] = pd.cut(sub[rating_col], bins=10)
	367	+ binned = sub.groupby("rating_bin", observed=True)[price_col].agg(
	368	+ ["mean", "sem", "count"]
	369	+ ).dropna()
	370	+ binned["mid"] = [interval.mid for interval in binned.index]
	371	+ ax.errorbar(binned["mid"], binned["mean"], yerr=1.96 * binned["sem"],
	372	+ fmt="o-", color="#4C72B0", capsize=3, markersize=5)
	373	+ ax.set_xlabel("Review Rating (binned)")
	374	+ ax.set_ylabel("Mean Price (CAD)")
	375	+ ax.set_title("Mean Price by Rating Bin (95% CI)")
	376	+ ax.yaxis.set_major_formatter(mticker.StrMethodFormatter("${x:,.0f}"))
	377	+
	378	+ fig.suptitle("Review Score and Price Relationship", y=1.02, fontsize=14)
	379	+ fig.tight_layout()
	380	+ return _save_figure(fig, "07_review_score_premium", save_dir)
	381	+
	382	+
	383	+# ---------------------------------------------------------------------------
	384	+# Figure 08 - Distance gradient
	385	+# ---------------------------------------------------------------------------
	386	+
	387	+def plot_distance_gradient(
	388	+ df: pd.DataFrame,
	389	+ save_dir: str,
	390	+ price_col: str = "price_numeric",
	391	+ distance_col: Optional[str] = None,
	392	+ lat_col: str = "lat",
	393	+ lon_col: str = "long",
	394	+ center_lat: Optional[float] = None,
	395	+ center_lon: Optional[float] = None,
	396	+ figsize: Tuple[int, int] = _DEFAULT_FIGSIZE,
	397	+) -> List[str]:
	398	+ """
	399	+ 08_distance_to_center_gradient.png -- Scatter + LOWESS of price vs
	400	+ distance to city centre.
	401	+
	402	+ If no distance column exists, one is computed from lat/long using a
	403	+ Haversine approximation to the centroid (or a supplied centre).
	404	+ """
	405	+ _apply_style()
	406	+ sub = df.dropna(subset=[price_col, lat_col, lon_col]).copy()
	407	+ sub = sub[sub[price_col] > 0]
	408	+
	409	+ if distance_col and distance_col in sub.columns:
	410	+ dist = sub[distance_col]
	411	+ else:
	412	+ # Compute distance to centroid (Haversine)
	413	+ if center_lat is None:
	414	+ center_lat = sub[lat_col].median()
	415	+ if center_lon is None:
	416	+ center_lon = sub[lon_col].median()
	417	+
	418	+ def _haversine_km(lat1: float, lon1: float,
	419	+ lat2: float, lon2: float) -> float:
	420	+ R = 6371.0
	421	+ dlat = np.radians(lat2 - lat1)
	422	+ dlon = np.radians(lon2 - lon1)
	423	+ a = (np.sin(dlat / 2) ** 2 +
	424	+ np.cos(np.radians(lat1)) * np.cos(np.radians(lat2)) *
	425	+ np.sin(dlon / 2) ** 2)
	426	+ return R * 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
	427	+
	428	+ dist = sub.apply(
	429	+ lambda r: _haversine_km(r[lat_col], r[lon_col],
	430	+ center_lat, center_lon),
	431	+ axis=1,
	432	+ )
	433	+ distance_col = "dist_to_center_km"
	434	+ sub[distance_col] = dist
	435	+
	436	+ fig, ax = plt.subplots(figsize=figsize)
	437	+ ax.scatter(sub[distance_col], sub[price_col],
	438	+ alpha=0.15, s=8, color="#4C72B0", edgecolors="none")
	439	+
	440	+ # LOWESS smoothing
	441	+ try:
	442	+ from statsmodels.nonparametric.smoothers_lowess import lowess
	443	+ sample = sub.sample(min(5000, len(sub)), random_state=42)
	444	+ lowess_result = lowess(
	445	+ sample[price_col].values,
	446	+ sample[distance_col].values,
	447	+ frac=0.3,
	448	+ )
	449	+ ax.plot(lowess_result[:, 0], lowess_result[:, 1],
	450	+ color="#C44E52", linewidth=2.5, label="LOWESS")
	451	+ ax.legend(frameon=True, fancybox=False, edgecolor="0.8")
	452	+ except ImportError:
	453	+ logger.warning("statsmodels not available; LOWESS line omitted.")
	454	+
	455	+ ax.set_xlabel("Distance to Centre (km)")
	456	+ ax.set_ylabel("Nightly Price (CAD)")
	457	+ ax.set_title("Price Gradient: Distance to City Centre\n"
	458	+ "(negative gradient expected from urban economics theory)")
	459	+ ax.yaxis.set_major_formatter(mticker.StrMethodFormatter("${x:,.0f}"))
	460	+
	461	+ fig.tight_layout()
	462	+ return _save_figure(fig, "08_distance_to_center_gradient", save_dir)
	463	+
	464	+
	465	+# ---------------------------------------------------------------------------
	466	+# Figure 09 - Quantile coefficients
	467	+# ---------------------------------------------------------------------------
	468	+
	469	+def plot_quantile_coefficients(
	470	+ coef_df: pd.DataFrame,
	471	+ save_dir: str,
	472	+ variables: Optional[List[str]] = None,
	473	+ figsize: Tuple[int, int] = (12, 8),
	474	+) -> List[str]:
	475	+ """
	476	+ 09_quantile_coefficients.png -- Coefficient evolution across quantiles.
	477	+
	478	+ Parameters
	479	+ ----------
	480	+ coef_df : pd.DataFrame
	481	+ Must contain columns 'quantile', 'variable', 'coef', and optionally
	482	+ 'ci_lower', 'ci_upper'.
	483	+ """
	484	+ _apply_style()
	485	+
	486	+ if variables is None:
	487	+ variables = coef_df["variable"].unique().tolist()[:6]
	488	+
	489	+ n_vars = len(variables)
	490	+ ncols = min(3, n_vars)
	491	+ nrows = int(np.ceil(n_vars / ncols))
	492	+
	493	+ fig, axes = plt.subplots(nrows, ncols, figsize=figsize, squeeze=False)
	494	+
	495	+ for idx, var in enumerate(variables):
	496	+ ax = axes[idx // ncols, idx % ncols]
	497	+ var_data = coef_df[coef_df["variable"] == var].sort_values("quantile")
	498	+
	499	+ ax.plot(var_data["quantile"], var_data["coef"],
	500	+ "o-", color="#4C72B0", markersize=5, linewidth=1.5)
	501	+
	502	+ if "ci_lower" in var_data.columns and "ci_upper" in var_data.columns:
	503	+ ax.fill_between(
	504	+ var_data["quantile"],
	505	+ var_data["ci_lower"],
	506	+ var_data["ci_upper"],
	507	+ alpha=0.2, color="#4C72B0",
	508	+ )
	509	+
	510	+ ax.axhline(0, color="black", linewidth=0.5, linestyle="--")
	511	+ ax.set_title(var, fontsize=10)
	512	+ ax.set_xlabel("Quantile")
	513	+ ax.set_ylabel("Coefficient")
	514	+
	515	+ # Hide unused axes
	516	+ for idx in range(n_vars, nrows * ncols):
	517	+ axes[idx // ncols, idx % ncols].set_visible(False)
	518	+
	519	+ fig.suptitle("Quantile Regression Coefficients Across the Price Distribution",
	520	+ y=1.02, fontsize=13)
	521	+ fig.tight_layout()
	522	+ return _save_figure(fig, "09_quantile_coefficients", save_dir)
	523	+
	524	+
	525	+# ---------------------------------------------------------------------------
	526	+# Figure 14 - Predicted vs actual
	527	+# ---------------------------------------------------------------------------
	528	+
	529	+def plot_predicted_vs_actual(
	530	+ y_true: Union[np.ndarray, pd.Series],
	531	+ y_pred: Union[np.ndarray, pd.Series],
	532	+ save_dir: str,
	533	+ model_name: str = "Model",
	534	+ figsize: Tuple[int, int] = _DEFAULT_FIGSIZE,
	535	+) -> List[str]:
	536	+ """14_predicted_vs_actual.png -- 45-degree line plot."""
	537	+ _apply_style()
	538	+
	539	+ y_true = np.asarray(y_true)
	540	+ y_pred = np.asarray(y_pred)
	541	+
	542	+ fig, ax = plt.subplots(figsize=figsize)
	543	+
	544	+ ax.scatter(y_true, y_pred, alpha=0.2, s=10, color="#4C72B0",
	545	+ edgecolors="none")
	546	+
	547	+ # 45-degree line
	548	+ lims = [
	549	+ min(y_true.min(), y_pred.min()),
	550	+ max(y_true.max(), y_pred.max()),
	551	+ ]
	552	+ ax.plot(lims, lims, "--", color="#C44E52", linewidth=1.5,
	553	+ label="Perfect prediction")
	554	+
	555	+ # Metrics
	556	+ from sklearn.metrics import r2_score, mean_absolute_error, \
	557	+ root_mean_squared_error
	558	+ r2 = r2_score(y_true, y_pred)
	559	+ mae = mean_absolute_error(y_true, y_pred)
	560	+ rmse = root_mean_squared_error(y_true, y_pred)
	561	+
	562	+ stats_text = f"$R^2$ = {r2:.4f}\nMAE = {mae:.2f}\nRMSE = {rmse:.2f}"
	563	+ ax.annotate(stats_text, xy=(0.05, 0.92), xycoords="axes fraction",
	564	+ fontsize=10, va="top",
	565	+ bbox=dict(boxstyle="round,pad=0.4", fc="white",
	566	+ ec="0.8", alpha=0.9))
	567	+
	568	+ ax.set_xlabel("Actual Values")
	569	+ ax.set_ylabel("Predicted Values")
	570	+ ax.set_title(f"Predicted vs. Actual -- {model_name}")
	571	+ ax.legend(loc="lower right", frameon=True, fancybox=False, edgecolor="0.8")
	572	+
	573	+ fig.tight_layout()
	574	+ return _save_figure(fig, "14_predicted_vs_actual", save_dir)
	575	+
	576	+
	577	+# ---------------------------------------------------------------------------
	578	+# Figure 15 - Hedonic decomposition (stacked bar)
	579	+# ---------------------------------------------------------------------------
	580	+
	581	+def plot_hedonic_decomposition(
	582	+ decomp_df: pd.DataFrame,
	583	+ save_dir: str,
	584	+ figsize: Tuple[int, int] = (12, 7),
	585	+) -> List[str]:
	586	+ """
	587	+ 15_hedonic_decomposition_example.png -- Stacked bar showing how
	588	+ predicted price is decomposed into attribute contributions.
	589	+
	590	+ Parameters
	591	+ ----------
	592	+ decomp_df : pd.DataFrame
	593	+ Rows = individual listings (or a small sample), columns = attribute
	594	+ categories (e.g., 'Location', 'Size', 'Amenities', 'Host',
	595	+ 'Intercept'). Values = contribution to predicted ln(price) or price.
	596	+ """
	597	+ _apply_style()
	598	+
	599	+ df = decomp_df.copy()
	600	+ if len(df) > 15:
	601	+ df = df.head(15)
	602	+
	603	+ # Ensure listing labels
	604	+ if df.index.dtype == "int64":
	605	+ df.index = [f"Listing {i+1}" for i in range(len(df))]
	606	+
	607	+ fig, ax = plt.subplots(figsize=figsize)
	608	+
	609	+ # Build stacked bars (positive contributions only for clarity)
	610	+ bottom = np.zeros(len(df))
	611	+ colours = sns.color_palette("Set2", n_colors=len(df.columns))
	612	+
	613	+ for i, col in enumerate(df.columns):
	614	+ vals = df[col].values.astype(float)
	615	+ ax.barh(df.index, vals, left=bottom, color=colours[i],
	616	+ edgecolor="white", linewidth=0.5, label=col)
	617	+ bottom += np.maximum(vals, 0)
	618	+
	619	+ ax.set_xlabel("Contribution to Predicted Price")
	620	+ ax.set_ylabel("")
	621	+ ax.set_title("Hedonic Price Decomposition\n"
	622	+ "(marginal contribution of each attribute category)")
	623	+ ax.legend(bbox_to_anchor=(1.01, 1), loc="upper left",
	624	+ frameon=True, fancybox=False, edgecolor="0.8")
	625	+
	626	+ fig.tight_layout()
	627	+ return _save_figure(fig, "15_hedonic_decomposition_example", save_dir)
	628	+
	629	+
	630	+# ---------------------------------------------------------------------------
	631	+# Master function
	632	+# ---------------------------------------------------------------------------
	633	+
	634	+def generate_all_figures(
	635	+ df: pd.DataFrame,
	636	+ results: Optional[Dict[str, Any]] = None,
	637	+ save_dir: Optional[str] = None,
	638	+ price_col: str = "price_numeric",
	639	+) -> Dict[str, List[str]]:
	640	+ """
	641	+ Generate all figures that can be produced from the dataset alone
	642	+ (figures requiring model results are skipped if results is None).
	643	+
	644	+ Parameters
	645	+ ----------
	646	+ df : pd.DataFrame
	647	+ Full analysis dataset.
	648	+ results : dict, optional
	649	+ ``{'premiums_df': pd.DataFrame, 'coef_df': pd.DataFrame,
	650	+ 'y_true': array, 'y_pred': array, 'decomp_df': pd.DataFrame,
	651	+ 'model_name': str}``.
	652	+ save_dir : str, optional
	653	+ Output directory; defaults to ``results/figures/``.
	654	+
	655	+ Returns
	656	+ -------
	657	+ dict
	658	+ ``{figure_name: [saved_paths]}``.
	659	+ """
	660	+ if save_dir is None:
	661	+ save_dir = os.path.join(
	662	+ os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
	663	+ "results", "figures",
	664	+ )
	665	+
	666	+ if results is None:
	667	+ results = {}
	668	+
	669	+ output: Dict[str, List[str]] = {}
	670	+ logger.info("Generating all figures to %s", save_dir)
	671	+
	672	+ # Data-only figures
	673	+ try:
	674	+ output["01_price_distribution"] = plot_price_distribution(
	675	+ df, save_dir, price_col=price_col)
	676	+ except Exception as e:
	677	+ logger.error("Failed 01_price_distribution: %s", e)
	678	+
	679	+ try:
	680	+ output["02_log_price_distribution"] = plot_log_price_distribution(
	681	+ df, save_dir, price_col=price_col)
	682	+ except Exception as e:
	683	+ logger.error("Failed 02_log_price_distribution: %s", e)
	684	+
	685	+ try:
	686	+ output["03_price_map"] = plot_price_map(
	687	+ df, save_dir, price_col=price_col)
	688	+ except Exception as e:
	689	+ logger.error("Failed 03_price_map: %s", e)
	690	+
	691	+ try:
	692	+ output["04_price_by_neighbourhood"] = plot_price_by_neighbourhood(
	693	+ df, save_dir, price_col=price_col)
	694	+ except Exception as e:
	695	+ logger.error("Failed 04_price_by_neighbourhood: %s", e)
	696	+
	697	+ try:
	698	+ output["05_price_by_room_type"] = plot_price_by_room_type(
	699	+ df, save_dir, price_col=price_col)
	700	+ except Exception as e:
	701	+ logger.error("Failed 05_price_by_room_type: %s", e)
	702	+
	703	+ # Review score premium (data-only)
	704	+ try:
	705	+ output["07_review_score_premium"] = plot_review_score_premium(
	706	+ df, save_dir, price_col=price_col)
	707	+ except Exception as e:
	708	+ logger.error("Failed 07_review_score_premium: %s", e)
	709	+
	710	+ # Distance gradient (data-only)
	711	+ try:
	712	+ output["08_distance_gradient"] = plot_distance_gradient(
	713	+ df, save_dir, price_col=price_col)
	714	+ except Exception as e:
	715	+ logger.error("Failed 08_distance_gradient: %s", e)
	716	+
	717	+ # Model-dependent figures
	718	+ if "premiums_df" in results:
	719	+ try:
	720	+ output["06_amenity_premiums"] = plot_amenity_premiums(
	721	+ results["premiums_df"], save_dir)
	722	+ except Exception as e:
	723	+ logger.error("Failed 06_amenity_premiums: %s", e)
	724	+
	725	+ if "coef_df" in results:
	726	+ try:
	727	+ output["09_quantile_coefficients"] = plot_quantile_coefficients(
	728	+ results["coef_df"], save_dir)
	729	+ except Exception as e:
	730	+ logger.error("Failed 09_quantile_coefficients: %s", e)
	731	+
	732	+ if "y_true" in results and "y_pred" in results:
	733	+ try:
	734	+ output["14_predicted_vs_actual"] = plot_predicted_vs_actual(
	735	+ results["y_true"], results["y_pred"], save_dir,
	736	+ model_name=results.get("model_name", "Model"))
	737	+ except Exception as e:
	738	+ logger.error("Failed 14_predicted_vs_actual: %s", e)
	739	+
	740	+ if "decomp_df" in results:
	741	+ try:
	742	+ output["15_hedonic_decomposition"] = plot_hedonic_decomposition(
	743	+ results["decomp_df"], save_dir)
	744	+ except Exception as e:
	745	+ logger.error("Failed 15_hedonic_decomposition: %s", e)
	746	+
	747	+ logger.info("Figure generation complete: %d/%d succeeded.",
	748	+ len(output), 11)
	749	+ return output

added src/geospatial.py +338 −0

		`@@ -0,0 +1,338 @@`
	1	+"""Geospatial feature engineering for hedonic pricing analysis."""
	2	+
	3	+from __future__ import annotations
	4	+
	5	+import logging
	6	+from typing import Any
	7	+
	8	+import numpy as np
	9	+import pandas as pd
	10	+from sklearn.cluster import KMeans
	11	+from sklearn.neighbors import BallTree
	12	+
	13	+logger = logging.getLogger(__name__)
	14	+
	15	+EARTH_RADIUS_M = 6_371_000.0
	16	+
	17	+# ---------------------------------------------------------------------------
	18	+# Default city centres (major Quebec cities)
	19	+# ---------------------------------------------------------------------------
	20	+
	21	+DEFAULT_CITY_CENTERS: dict[str, tuple[float, float]] = {
	22	+ "montréal": (45.5017, -73.5673),
	23	+ "montreal": (45.5017, -73.5673),
	24	+ "québec": (46.8139, -71.2080),
	25	+ "quebec": (46.8139, -71.2080),
	26	+ "gatineau": (45.4765, -75.7013),
	27	+ "sherbrooke": (45.4042, -71.8929),
	28	+ "saguenay": (48.4280, -71.0686),
	29	+ "laval": (45.6066, -73.7124),
	30	+ "trois-rivières": (46.3432, -72.5432),
	31	+ "trois-rivieres": (46.3432, -72.5432),
	32	+ "lévis": (46.8032, -71.1780),
	33	+ "levis": (46.8032, -71.1780),
	34	+ "terrebonne": (45.6960, -73.6473),
	35	+ "saint-jean-sur-richelieu": (45.3073, -73.2628),
	36	+ "repentigny": (45.7402, -73.4598),
	37	+ "drummondville": (45.8838, -72.4843),
	38	+ "granby": (45.4000, -72.7333),
	39	+ "saint-hyacinthe": (45.6307, -72.9571),
	40	+ "rimouski": (48.4490, -68.5233),
	41	+ "victoriaville": (46.0500, -71.9667),
	42	+ "saint-georges": (46.1167, -70.6667),
	43	+ "matane": (48.8500, -67.5333),
	44	+ "mont-tremblant": (46.2103, -74.5847),
	45	+}
	46	+
	47	+# ---------------------------------------------------------------------------
	48	+# Haversine helpers
	49	+# ---------------------------------------------------------------------------
	50	+
	51	+def _to_radians(lat: np.ndarray, lon: np.ndarray) -> np.ndarray:
	52	+ """Convert lat/lon arrays to radians, shape (n, 2)."""
	53	+ return np.column_stack([np.radians(lat), np.radians(lon)])
	54	+
	55	+
	56	+def _haversine_vector(lat1: np.ndarray, lon1: np.ndarray, lat2: float, lon2: float) -> np.ndarray:
	57	+ """Vectorised haversine distance in metres."""
	58	+ lat1_r, lon1_r = np.radians(lat1), np.radians(lon1)
	59	+ lat2_r, lon2_r = np.radians(lat2), np.radians(lon2)
	60	+ dlat = lat2_r - lat1_r
	61	+ dlon = lon2_r - lon1_r
	62	+ a = np.sin(dlat / 2) ** 2 + np.cos(lat1_r) * np.cos(lat2_r) * np.sin(dlon / 2) ** 2
	63	+ return 2 * EARTH_RADIUS_M * np.arcsin(np.sqrt(a))
	64	+
	65	+
	66	+# ---------------------------------------------------------------------------
	67	+# BallTree construction
	68	+# ---------------------------------------------------------------------------
	69	+
	70	+def _build_balltree(lat: np.ndarray, lon: np.ndarray) -> BallTree:
	71	+ """Build a BallTree from latitude/longitude in radians using haversine metric."""
	72	+ coords_rad = _to_radians(lat, lon)
	73	+ return BallTree(coords_rad, metric="haversine")
	74	+
	75	+
	76	+# ---------------------------------------------------------------------------
	77	+# Distance to city centre
	78	+# ---------------------------------------------------------------------------
	79	+
	80	+def compute_distance_to_center(
	81	+ df: pd.DataFrame,
	82	+ city_centers_dict: dict[str, tuple[float, float]] \| None = None,
	83	+) -> pd.Series:
	84	+ """Compute haversine distance (m) from each listing to its city centre."""
	85	+ centers = city_centers_dict or DEFAULT_CITY_CENTERS
	86	+
	87	+ distances = pd.Series(np.nan, index=df.index, dtype=float)
	88	+ city_col = df["city"].str.lower().str.strip()
	89	+
	90	+ for city_key, (clat, clon) in centers.items():
	91	+ mask = city_col == city_key
	92	+ if mask.any():
	93	+ d = _haversine_vector(
	94	+ df.loc[mask, "lat"].values,
	95	+ df.loc[mask, "long"].values,
	96	+ clat,
	97	+ clon,
	98	+ )
	99	+ distances.loc[mask] = d
	100	+
	101	+ # For cities not in the dictionary, use the median of their own coordinates as centre
	102	+ missing_mask = distances.isna()
	103	+ if missing_mask.any():
	104	+ for city_name, grp in df.loc[missing_mask].groupby(city_col[missing_mask]):
	105	+ clat = grp["lat"].median()
	106	+ clon = grp["long"].median()
	107	+ d = _haversine_vector(grp["lat"].values, grp["long"].values, clat, clon)
	108	+ distances.loc[grp.index] = d
	109	+
	110	+ return distances
	111	+
	112	+
	113	+# ---------------------------------------------------------------------------
	114	+# Local density
	115	+# ---------------------------------------------------------------------------
	116	+
	117	+def compute_local_density(df: pd.DataFrame, radius_m: float = 1000.0) -> pd.Series:
	118	+ """Count of other listings within radius_m metres using BallTree."""
	119	+ valid = df[["lat", "long"]].notna().all(axis=1)
	120	+ sub = df.loc[valid]
	121	+ lat = sub["lat"].values
	122	+ lon = sub["long"].values
	123	+ tree = _build_balltree(lat, lon)
	124	+
	125	+ radius_rad = radius_m / EARTH_RADIUS_M
	126	+ coords_rad = _to_radians(lat, lon)
	127	+ counts = tree.query_radius(coords_rad, r=radius_rad, count_only=True)
	128	+
	129	+ density = np.maximum(counts - 1, 0)
	130	+ logger.info("Local density (r=%dm): mean=%.1f, max=%d", radius_m, density.mean(), density.max())
	131	+ result = pd.Series(np.nan, index=df.index, name=f"local_density_{int(radius_m)}m")
	132	+ result.loc[valid] = density
	133	+ return result
	134	+
	135	+
	136	+# ---------------------------------------------------------------------------
	137	+# Local average price
	138	+# ---------------------------------------------------------------------------
	139	+
	140	+def compute_local_avg_price(df: pd.DataFrame, radius_m: float = 1000.0) -> pd.Series:
	141	+ """Average price of other listings within radius_m metres."""
	142	+ coord_valid = df[["lat", "long"]].notna().all(axis=1)
	143	+ sub = df.loc[coord_valid]
	144	+ lat = sub["lat"].values
	145	+ lon = sub["long"].values
	146	+ prices = sub["price_numeric"].values.copy() if "price_numeric" in sub.columns else np.full(len(sub), np.nan)
	147	+ tree = _build_balltree(lat, lon)
	148	+
	149	+ radius_rad = radius_m / EARTH_RADIUS_M
	150	+ coords_rad = _to_radians(lat, lon)
	151	+ indices_list = tree.query_radius(coords_rad, r=radius_rad)
	152	+
	153	+ avg_prices = np.full(len(sub), np.nan)
	154	+ for i, neighbours in enumerate(indices_list):
	155	+ others = neighbours[neighbours != i]
	156	+ neighbour_prices = prices[others]
	157	+ valid = neighbour_prices[~np.isnan(neighbour_prices)]
	158	+ if len(valid) > 0:
	159	+ avg_prices[i] = valid.mean()
	160	+
	161	+ result = pd.Series(np.nan, index=df.index, name=f"local_avg_price_{int(radius_m)}m")
	162	+ result.loc[coord_valid] = avg_prices
	163	+ logger.info("Local avg price (r=%dm): computed for %d listings", radius_m, np.sum(~np.isnan(avg_prices)))
	164	+ return result
	165	+
	166	+
	167	+# ---------------------------------------------------------------------------
	168	+# Spatial lag (KNN)
	169	+# ---------------------------------------------------------------------------
	170	+
	171	+def compute_spatial_lag(df: pd.DataFrame, col: str, k: int = 5) -> pd.Series:
	172	+ """K-nearest-neighbour spatial lag of any numeric column."""
	173	+ coord_valid = df[["lat", "long"]].notna().all(axis=1)
	174	+ sub = df.loc[coord_valid]
	175	+ lat = sub["lat"].values
	176	+ lon = sub["long"].values
	177	+ values = sub[col].values.astype(float)
	178	+ tree = _build_balltree(lat, lon)
	179	+
	180	+ coords_rad = _to_radians(lat, lon)
	181	+ dists, indices = tree.query(coords_rad, k=k + 1)
	182	+
	183	+ lag = np.full(len(sub), np.nan)
	184	+ for i in range(len(sub)):
	185	+ nbr_idx = indices[i, 1:]
	186	+ nbr_vals = values[nbr_idx]
	187	+ valid = nbr_vals[~np.isnan(nbr_vals)]
	188	+ if len(valid) > 0:
	189	+ lag[i] = valid.mean()
	190	+
	191	+ result = pd.Series(np.nan, index=df.index, name=f"spatial_lag_{col}_k{k}")
	192	+ result.loc[coord_valid] = lag
	193	+ logger.info("Spatial lag of '%s' (k=%d): computed for %d listings", col, k, np.sum(~np.isnan(lag)))
	194	+ return result
	195	+
	196	+
	197	+# ---------------------------------------------------------------------------
	198	+# Spatial clusters
	199	+# ---------------------------------------------------------------------------
	200	+
	201	+def create_spatial_clusters(df: pd.DataFrame, n_clusters: int = 20) -> pd.Series:
	202	+ """K-means clustering on lat/long coordinates."""
	203	+ coords = df[["lat", "long"]].values
	204	+ valid_mask = ~np.isnan(coords).any(axis=1)
	205	+
	206	+ labels = pd.Series(-1, index=df.index, dtype=int, name="spatial_cluster")
	207	+ if valid_mask.sum() < n_clusters:
	208	+ logger.warning("Too few valid coordinates (%d) for %d clusters", valid_mask.sum(), n_clusters)
	209	+ return labels
	210	+
	211	+ kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
	212	+ kmeans.fit(coords[valid_mask])
	213	+ labels.iloc[np.where(valid_mask)[0]] = kmeans.labels_
	214	+
	215	+ logger.info("Spatial clusters: %d clusters from %d valid points", n_clusters, valid_mask.sum())
	216	+ return labels
	217	+
	218	+
	219	+# ---------------------------------------------------------------------------
	220	+# Combined distance features
	221	+# ---------------------------------------------------------------------------
	222	+
	223	+def compute_distance_features(
	224	+ df: pd.DataFrame,
	225	+ city_centers_dict: dict[str, tuple[float, float]] \| None = None,
	226	+) -> pd.DataFrame:
	227	+ """Compute the standard suite of distance-based features."""
	228	+ out = df.copy()
	229	+
	230	+ out["distance_to_center"] = compute_distance_to_center(out, city_centers_dict)
	231	+ out["log_distance_to_center"] = np.log1p(out["distance_to_center"])
	232	+
	233	+ out["local_density_500m"] = compute_local_density(out, radius_m=500.0)
	234	+ out["local_density_1km"] = compute_local_density(out, radius_m=1000.0)
	235	+
	236	+ out["local_avg_price_500m"] = compute_local_avg_price(out, radius_m=500.0)
	237	+ out["local_avg_price_1km"] = compute_local_avg_price(out, radius_m=1000.0)
	238	+
	239	+ out["spatial_cluster"] = create_spatial_clusters(out)
	240	+
	241	+ logger.info("Distance features complete: %d new columns", out.shape[1] - df.shape[1])
	242	+ return out
	243	+
	244	+
	245	+# ---------------------------------------------------------------------------
	246	+# Spatial weights matrix
	247	+# ---------------------------------------------------------------------------
	248	+
	249	+def build_spatial_weights(df: pd.DataFrame, k: int = 5) -> np.ndarray:
	250	+ """Build a row-standardised KNN spatial weights matrix (n x n)."""
	251	+ lat = df["lat"].values
	252	+ lon = df["long"].values
	253	+ tree = _build_balltree(lat, lon)
	254	+ coords_rad = _to_radians(lat, lon)
	255	+
	256	+ n = len(df)
	257	+ W = np.zeros((n, n), dtype=float)
	258	+
	259	+ _, indices = tree.query(coords_rad, k=k + 1)
	260	+ for i in range(n):
	261	+ nbrs = indices[i, 1:] # exclude self
	262	+ W[i, nbrs] = 1.0
	263	+
	264	+ # Row-standardise
	265	+ row_sums = W.sum(axis=1, keepdims=True)
	266	+ row_sums[row_sums == 0] = 1.0
	267	+ W = W / row_sums
	268	+
	269	+ logger.info("Spatial weights matrix built: %dx%d, k=%d", n, n, k)
	270	+ return W
	271	+
	272	+
	273	+# ---------------------------------------------------------------------------
	274	+# Moran's I
	275	+# ---------------------------------------------------------------------------
	276	+
	277	+def compute_morans_i(
	278	+ df: pd.DataFrame,
	279	+ col: str,
	280	+ w: np.ndarray \| None = None,
	281	+ k: int = 5,
	282	+) -> dict[str, float]:
	283	+ """Compute Moran's I statistic for spatial autocorrelation."""
	284	+ y = df[col].values.astype(float)
	285	+ valid_mask = ~np.isnan(y)
	286	+
	287	+ if valid_mask.sum() < 10:
	288	+ logger.warning("Too few valid observations (%d) for Moran's I", valid_mask.sum())
	289	+ return {"morans_i": np.nan, "expected_i": np.nan, "z_score": np.nan}
	290	+
	291	+ if w is None:
	292	+ w = build_spatial_weights(df, k=k)
	293	+
	294	+ # Restrict to valid observations
	295	+ idx = np.where(valid_mask)[0]
	296	+ y_sub = y[idx]
	297	+ w_sub = w[np.ix_(idx, idx)]
	298	+
	299	+ # Re-row-standardise the sub-matrix
	300	+ row_sums = w_sub.sum(axis=1, keepdims=True)
	301	+ row_sums[row_sums == 0] = 1.0
	302	+ w_sub = w_sub / row_sums
	303	+
	304	+ n = len(y_sub)
	305	+ y_mean = y_sub.mean()
	306	+ z = y_sub - y_mean
	307	+
	308	+ numerator = float(n * z @ w_sub @ z)
	309	+ denominator = float(w_sub.sum() * (z ** 2).sum())
	310	+
	311	+ if denominator == 0:
	312	+ return {"morans_i": np.nan, "expected_i": np.nan, "z_score": np.nan}
	313	+
	314	+ I = numerator / denominator
	315	+ expected_I = -1.0 / (n - 1)
	316	+
	317	+ # Variance under normality assumption
	318	+ S0 = w_sub.sum()
	319	+ S1 = 0.5 * ((w_sub + w_sub.T) ** 2).sum()
	320	+ S2 = ((w_sub.sum(axis=1) + w_sub.sum(axis=0)) ** 2).sum()
	321	+ k2 = (n * (z 4).sum()) / ((z 2).sum() ** 2)
	322	+
	323	+ A = n * ((n ** 2 - 3 * n + 3) * S1 - n * S2 + 3 * S0 ** 2)
	324	+ B = k2 * ((n ** 2 - n) * S1 - 2 * n * S2 + 6 * S0 ** 2)
	325	+ C = (n - 1) * (n - 2) * (n - 3) * S0 ** 2
	326	+
	327	+ if C == 0:
	328	+ z_score = np.nan
	329	+ else:
	330	+ var_I = (A - B) / C - expected_I ** 2
	331	+ if var_I > 0:
	332	+ z_score = float((I - expected_I) / np.sqrt(var_I))
	333	+ else:
	334	+ z_score = np.nan
	335	+
	336	+ result = {"morans_i": float(I), "expected_i": float(expected_I), "z_score": z_score}
	337	+ logger.info("Moran's I for '%s': I=%.4f, E[I]=%.4f, z=%.2f", col, I, expected_I, z_score if not np.isnan(z_score) else 0.0)
	338	+ return result

added src/hedonic_models.py +321 −0

		`@@ -0,0 +1,321 @@`
	1	+"""Core hedonic regression models for Airbnb pricing analysis."""
	2	+
	3	+import logging
	4	+from typing import Dict, List, Optional, Tuple
	5	+
	6	+import numpy as np
	7	+import pandas as pd
	8	+import statsmodels.api as sm
	9	+from sklearn.model_selection import KFold
	10	+from sklearn.metrics import mean_squared_error, mean_absolute_error
	11	+
	12	+logger = logging.getLogger(__name__)
	13	+
	14	+# ---------------------------------------------------------------------------
	15	+# Feature group definitions for the 9-model nested sequence
	16	+# ---------------------------------------------------------------------------
	17	+
	18	+STRUCTURAL_COLS = [
	19	+ "bedrooms", "bathrooms", "guests_count", "is_entire_home",
	20	+ "is_private_room", "log_accommodates", "log_bedrooms", "log_bathrooms",
	21	+ "bathrooms_per_guest", "bedrooms_per_guest", "large_capacity_listing",
	22	+ "accommodates_sq", "bedrooms_sq", "bathrooms_sq",
	23	+]
	24	+
	25	+AMENITY_COLS = [
	26	+ "amenities_count", "amenity_richness_index", "luxury_index",
	27	+ "business_travel_index", "family_comfort_index", "leisure_index",
	28	+ "remote_work_index", "pets_allowed",
	29	+]
	30	+
	31	+HOST_COLS = [
	32	+ "is_superhost", "superhost_flag", "host_professional_dummy",
	33	+ "multi_listing_host", "superhost_x_entire_home", "guest_favorite_flag",
	34	+ "is_guest_favorite",
	35	+]
	36	+
	37	+REVIEW_COLS = [
	38	+ "rating", "num_reviews", "log_reviews", "review_quality",
	39	+ "reputation_score", "cleanliness_premium", "location_rating_premium",
	40	+ "has_reviews", "quality_score",
	41	+]
	42	+
	43	+LOCATION_COLS = [
	44	+ "distance_to_center", "lat", "long",
	45	+]
	46	+
	47	+NEIGHBOURHOOD_COLS = [
	48	+ "spatial_cluster",
	49	+]
	50	+
	51	+NLP_COLS = [
	52	+ "title_length", "title_word_count", "luxury_language_score",
	53	+ "cozy_language_score", "professional_tone_score", "tourism_language_score",
	54	+ "text_embed_0", "text_embed_1", "text_embed_2", "text_embed_3",
	55	+]
	56	+
	57	+SPATIAL_CONTROL_COLS = [
	58	+ "local_density_500m", "local_density_1km",
	59	+ "local_avg_price_500m", "local_avg_price_1km",
	60	+]
	61	+
	62	+MODEL_SEQUENCE = {
	63	+ "Model 1 - Structural": STRUCTURAL_COLS,
	64	+ "Model 2 - + Amenities": STRUCTURAL_COLS + AMENITY_COLS,
	65	+ "Model 3 - + Host": STRUCTURAL_COLS + AMENITY_COLS + HOST_COLS,
	66	+ "Model 4 - + Reviews": STRUCTURAL_COLS + AMENITY_COLS + HOST_COLS + REVIEW_COLS,
	67	+ "Model 5 - + Location": STRUCTURAL_COLS + AMENITY_COLS + HOST_COLS + REVIEW_COLS + LOCATION_COLS,
	68	+ "Model 6 - + Neighbourhood": STRUCTURAL_COLS + AMENITY_COLS + HOST_COLS + REVIEW_COLS + LOCATION_COLS + NEIGHBOURHOOD_COLS,
	69	+ "Model 7 - + NLP": STRUCTURAL_COLS + AMENITY_COLS + HOST_COLS + REVIEW_COLS + LOCATION_COLS + NEIGHBOURHOOD_COLS + NLP_COLS,
	70	+ "Model 8 - + Spatial": STRUCTURAL_COLS + AMENITY_COLS + HOST_COLS + REVIEW_COLS + LOCATION_COLS + NEIGHBOURHOOD_COLS + NLP_COLS + SPATIAL_CONTROL_COLS,
	71	+ "Model 9 - Full Hedonic": STRUCTURAL_COLS + AMENITY_COLS + HOST_COLS + REVIEW_COLS + LOCATION_COLS + NEIGHBOURHOOD_COLS + NLP_COLS + SPATIAL_CONTROL_COLS,
	72	+}
	73	+
	74	+
	75	+def _available_cols(df: pd.DataFrame, requested: List[str]) -> List[str]:
	76	+ """Return the subset of requested columns that actually exist in df."""
	77	+ present = [c for c in requested if c in df.columns]
	78	+ missing = set(requested) - set(present)
	79	+ if missing:
	80	+ logger.warning("Missing columns (will be skipped): %s", sorted(missing))
	81	+ return present
	82	+
	83	+
	84	+def _prepare_data(
	85	+ df: pd.DataFrame,
	86	+ y_col: str,
	87	+ x_cols: List[str],
	88	+) -> Tuple[pd.Series, pd.DataFrame]:
	89	+ """Drop rows with NaN in y or X; add constant; return (y, X)."""
	90	+ cols = _available_cols(df, x_cols)
	91	+ if not cols:
	92	+ raise ValueError("No valid feature columns found in the DataFrame.")
	93	+ subset = df[[y_col] + cols].dropna()
	94	+ if subset.empty:
	95	+ raise ValueError("No observations remaining after dropping NaN rows.")
	96	+ y = subset[y_col].astype(float)
	97	+ X = sm.add_constant(subset[cols].astype(float))
	98	+ logger.info("Prepared data: n=%d, k=%d (excl. constant)", len(y), len(cols))
	99	+ return y, X
	100	+
	101	+
	102	+# ---------------------------------------------------------------------------
	103	+# OLS estimators
	104	+# ---------------------------------------------------------------------------
	105	+
	106	+def estimate_ols(
	107	+ df: pd.DataFrame,
	108	+ y_col: str,
	109	+ x_cols: List[str],
	110	+ robust: str = "HC3",
	111	+) -> sm.regression.linear_model.RegressionResultsWrapper:
	112	+ """OLS with heteroskedasticity-robust standard errors."""
	113	+ y, X = _prepare_data(df, y_col, x_cols)
	114	+ model = sm.OLS(y, X)
	115	+ results = model.fit(cov_type=robust)
	116	+ logger.info(
	117	+ "OLS (%s): R2=%.4f, Adj-R2=%.4f, n=%d",
	118	+ robust, results.rsquared, results.rsquared_adj, int(results.nobs),
	119	+ )
	120	+ return results
	121	+
	122	+
	123	+def estimate_ols_clustered(
	124	+ df: pd.DataFrame,
	125	+ y_col: str,
	126	+ x_cols: List[str],
	127	+ cluster_col: str,
	128	+) -> sm.regression.linear_model.RegressionResultsWrapper:
	129	+ """OLS with cluster-robust standard errors."""
	130	+ cols = _available_cols(df, x_cols)
	131	+ subset = df[[y_col] + cols + [cluster_col]].dropna()
	132	+ if subset.empty:
	133	+ raise ValueError("No observations remaining after dropping NaN rows.")
	134	+ y = subset[y_col].astype(float)
	135	+ X = sm.add_constant(subset[cols].astype(float))
	136	+ groups = subset[cluster_col]
	137	+ model = sm.OLS(y, X)
	138	+ results = model.fit(cov_type="cluster", cov_kwds={"groups": groups})
	139	+ logger.info(
	140	+ "OLS (clustered on %s): R2=%.4f, n=%d, clusters=%d",
	141	+ cluster_col, results.rsquared, int(results.nobs), groups.nunique(),
	142	+ )
	143	+ return results
	144	+
	145	+
	146	+# ---------------------------------------------------------------------------
	147	+# Nested model sequence
	148	+# ---------------------------------------------------------------------------
	149	+
	150	+def build_model_sequence(
	151	+ df: pd.DataFrame,
	152	+ y_col: str = "log_price",
	153	+ robust: str = "HC3",
	154	+) -> Dict[str, sm.regression.linear_model.RegressionResultsWrapper]:
	155	+ """Estimate the 9-model nested hedonic sequence."""
	156	+ results: Dict[str, sm.regression.linear_model.RegressionResultsWrapper] = {}
	157	+ for name, xcols in MODEL_SEQUENCE.items():
	158	+ avail = _available_cols(df, xcols)
	159	+ if not avail:
	160	+ logger.warning("Skipping %s: no available features.", name)
	161	+ continue
	162	+ try:
	163	+ res = estimate_ols(df, y_col, avail, robust=robust)
	164	+ results[name] = res
	165	+ logger.info("Estimated %s (k=%d)", name, len(avail))
	166	+ except Exception as exc:
	167	+ logger.error("Failed to estimate %s: %s", name, exc)
	168	+ return results
	169	+
	170	+
	171	+# ---------------------------------------------------------------------------
	172	+# Model comparison & diagnostics
	173	+# ---------------------------------------------------------------------------
	174	+
	175	+def compare_models(
	176	+ results_dict: Dict[str, sm.regression.linear_model.RegressionResultsWrapper],
	177	+) -> pd.DataFrame:
	178	+ """Extract R2, adj-R2, RMSE, MAE, AIC, BIC for each model."""
	179	+ rows = []
	180	+ for name, res in results_dict.items():
	181	+ resid = res.resid
	182	+ n = int(res.nobs)
	183	+ rmse = np.sqrt(np.mean(resid ** 2))
	184	+ mae = np.mean(np.abs(resid))
	185	+ rows.append({
	186	+ "model": name,
	187	+ "n_obs": n,
	188	+ "n_params": int(res.df_model) + 1,
	189	+ "R2": round(res.rsquared, 6),
	190	+ "Adj_R2": round(res.rsquared_adj, 6),
	191	+ "RMSE": round(rmse, 6),
	192	+ "MAE": round(mae, 6),
	193	+ "AIC": round(res.aic, 2),
	194	+ "BIC": round(res.bic, 2),
	195	+ })
	196	+ comp = pd.DataFrame(rows)
	197	+ logger.info("Model comparison table built (%d models).", len(comp))
	198	+ return comp
	199	+
	200	+
	201	+def compute_price_premiums(
	202	+ results: sm.regression.linear_model.RegressionResultsWrapper,
	203	+) -> pd.DataFrame:
	204	+ """Convert log-linear coefficients to percentage price premiums: 100*(exp(b)-1)."""
	205	+ params = results.params.drop("const", errors="ignore")
	206	+ se = results.bse.drop("const", errors="ignore")
	207	+ pvals = results.pvalues.drop("const", errors="ignore")
	208	+ ci = results.conf_int().drop("const", errors="ignore")
	209	+
	210	+ premium = 100.0 * (np.exp(params) - 1)
	211	+ premium_lo = 100.0 * (np.exp(ci[0]) - 1)
	212	+ premium_hi = 100.0 * (np.exp(ci[1]) - 1)
	213	+
	214	+ df_out = pd.DataFrame({
	215	+ "coefficient": params.values,
	216	+ "std_error": se.values,
	217	+ "p_value": pvals.values,
	218	+ "premium_pct": premium.values,
	219	+ "premium_lo_95": premium_lo.values,
	220	+ "premium_hi_95": premium_hi.values,
	221	+ }, index=params.index)
	222	+ df_out = df_out.sort_values("premium_pct", ascending=False)
	223	+ logger.info("Computed price premiums for %d variables.", len(df_out))
	224	+ return df_out
	225	+
	226	+
	227	+# ---------------------------------------------------------------------------
	228	+# Cross-validation
	229	+# ---------------------------------------------------------------------------
	230	+
	231	+def cross_validate_ols(
	232	+ df: pd.DataFrame,
	233	+ y_col: str,
	234	+ x_cols: List[str],
	235	+ k: int = 5,
	236	+ random_state: int = 42,
	237	+) -> Dict[str, float]:
	238	+ """K-fold cross-validation returning out-of-sample RMSE and MAE."""
	239	+ y, X = _prepare_data(df, y_col, x_cols)
	240	+ kf = KFold(n_splits=k, shuffle=True, random_state=random_state)
	241	+ rmses, maes = [], []
	242	+ for fold, (train_idx, test_idx) in enumerate(kf.split(X), 1):
	243	+ X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
	244	+ y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
	245	+ model = sm.OLS(y_train, X_train).fit()
	246	+ y_pred = model.predict(X_test)
	247	+ rmses.append(np.sqrt(mean_squared_error(y_test, y_pred)))
	248	+ maes.append(mean_absolute_error(y_test, y_pred))
	249	+ logger.debug("Fold %d: RMSE=%.4f, MAE=%.4f", fold, rmses[-1], maes[-1])
	250	+
	251	+ result = {
	252	+ "mean_rmse": float(np.mean(rmses)),
	253	+ "std_rmse": float(np.std(rmses)),
	254	+ "mean_mae": float(np.mean(maes)),
	255	+ "std_mae": float(np.std(maes)),
	256	+ "k": k,
	257	+ }
	258	+ logger.info(
	259	+ "%d-fold CV: RMSE=%.4f (+/-%.4f), MAE=%.4f (+/-%.4f)",
	260	+ k, result["mean_rmse"], result["std_rmse"],
	261	+ result["mean_mae"], result["std_mae"],
	262	+ )
	263	+ return result
	264	+
	265	+
	266	+# ---------------------------------------------------------------------------
	267	+# Publication-quality regression table
	268	+# ---------------------------------------------------------------------------
	269	+
	270	+def format_regression_table(
	271	+ results_dict: Dict[str, sm.regression.linear_model.RegressionResultsWrapper],
	272	+ stars: bool = True,
	273	+) -> pd.DataFrame:
	274	+ """Create a publication-ready regression comparison table (coef + SE rows)."""
	275	+ all_vars: List[str] = []
	276	+ for res in results_dict.values():
	277	+ for v in res.params.index:
	278	+ if v not in all_vars:
	279	+ all_vars.append(v)
	280	+
	281	+ rows: List[Dict[str, str]] = []
	282	+ for var in all_vars:
	283	+ coef_row: Dict[str, str] = {"variable": var}
	284	+ se_row: Dict[str, str] = {"variable": ""}
	285	+ for mname, res in results_dict.items():
	286	+ if var in res.params.index:
	287	+ b = res.params[var]
	288	+ s = res.bse[var]
	289	+ p = res.pvalues[var]
	290	+ star_str = ""
	291	+ if stars:
	292	+ if p < 0.001:
	293	+ star_str = "***"
	294	+ elif p < 0.01:
	295	+ star_str = "**"
	296	+ elif p < 0.05:
	297	+ star_str = "*"
	298	+ coef_row[mname] = f"{b:.4f}{star_str}"
	299	+ se_row[mname] = f"({s:.4f})"
	300	+ else:
	301	+ coef_row[mname] = ""
	302	+ se_row[mname] = ""
	303	+ rows.append(coef_row)
	304	+ rows.append(se_row)
	305	+
	306	+ # Footer rows
	307	+ for stat_name, extractor in [
	308	+ ("N", lambda r: f"{int(r.nobs)}"),
	309	+ ("R2", lambda r: f"{r.rsquared:.4f}"),
	310	+ ("Adj. R2", lambda r: f"{r.rsquared_adj:.4f}"),
	311	+ ("AIC", lambda r: f"{r.aic:.1f}"),
	312	+ ("BIC", lambda r: f"{r.bic:.1f}"),
	313	+ ]:
	314	+ row: Dict[str, str] = {"variable": stat_name}
	315	+ for mname, res in results_dict.items():
	316	+ row[mname] = extractor(res)
	317	+ rows.append(row)
	318	+
	319	+ table = pd.DataFrame(rows)
	320	+ logger.info("Formatted regression table: %d rows x %d models.", len(table), len(results_dict))
	321	+ return table

added src/ml_models.py +841 −0

		`@@ -0,0 +1,841 @@`
	1	+"""
	2	+ml_models.py
	3	+============
	4	+Machine learning models for hedonic pricing analysis of Airbnb listings.
	5	+
	6	+Provides training, evaluation, cross-validation, feature importance extraction,
	7	+and hyperparameter tuning for a battery of regression models including
	8	+ElasticNet, Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost,
	9	+and Generalized Additive Models (GAM).
	10	+
	11	+Author: Simon-Pierre Boucher
	12	+Date: 2026-05-20
	13	+"""
	14	+
	15	+from __future__ import annotations
	16	+
	17	+import logging
	18	+import warnings
	19	+from typing import Any, Dict, List, Optional, Tuple, Union
	20	+
	21	+import numpy as np
	22	+import pandas as pd
	23	+from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
	24	+from sklearn.linear_model import ElasticNetCV
	25	+from sklearn.metrics import (
	26	+ mean_absolute_error,
	27	+ mean_absolute_percentage_error,
	28	+ mean_squared_error,
	29	+ r2_score,
	30	+)
	31	+from sklearn.model_selection import (
	32	+ KFold,
	33	+ RandomizedSearchCV,
	34	+ cross_val_score,
	35	+ train_test_split,
	36	+)
	37	+from sklearn.pipeline import Pipeline
	38	+from sklearn.preprocessing import PolynomialFeatures, StandardScaler
	39	+
	40	+logger = logging.getLogger(__name__)
	41	+
	42	+# ---------------------------------------------------------------------------
	43	+# Data Preparation
	44	+# ---------------------------------------------------------------------------
	45	+
	46	+
	47	+def prepare_ml_data(
	48	+ df: pd.DataFrame,
	49	+ y_col: str,
	50	+ x_cols: List[str],
	51	+ test_size: float = 0.2,
	52	+ random_state: int = 42,
	53	+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, List[str]]:
	54	+ """Split data into train/test sets with NaN handling.
	55	+
	56	+ Parameters
	57	+ ----------
	58	+ df : pd.DataFrame
	59	+ Source dataframe.
	60	+ y_col : str
	61	+ Name of the target (price) column.
	62	+ x_cols : list[str]
	63	+ Feature column names.
	64	+ test_size : float
	65	+ Fraction held out for testing.
	66	+ random_state : int
	67	+ Seed for reproducibility.
	68	+
	69	+ Returns
	70	+ -------
	71	+ X_train, X_test, y_train, y_test : np.ndarray
	72	+ Split arrays.
	73	+ feature_names : list[str]
	74	+ Column names retained after NaN removal.
	75	+ """
	76	+ subset = df[x_cols + [y_col]].copy()
	77	+
	78	+ n_before = len(subset)
	79	+ subset = subset.dropna()
	80	+ n_after = len(subset)
	81	+ if n_before != n_after:
	82	+ logger.info(
	83	+ "Dropped %d rows containing NaN (%d -> %d).",
	84	+ n_before - n_after,
	85	+ n_before,
	86	+ n_after,
	87	+ )
	88	+
	89	+ X = subset[x_cols].values.astype(np.float64)
	90	+ y = subset[y_col].values.astype(np.float64)
	91	+ feature_names = list(x_cols)
	92	+
	93	+ X_train, X_test, y_train, y_test = train_test_split(
	94	+ X, y, test_size=test_size, random_state=random_state
	95	+ )
	96	+
	97	+ logger.info(
	98	+ "Train: %d samples, Test: %d samples, Features: %d",
	99	+ X_train.shape[0],
	100	+ X_test.shape[0],
	101	+ X_train.shape[1],
	102	+ )
	103	+ return X_train, X_test, y_train, y_test, feature_names
	104	+
	105	+
	106	+# ---------------------------------------------------------------------------
	107	+# Individual Model Trainers
	108	+# ---------------------------------------------------------------------------
	109	+
	110	+
	111	+def train_elastic_net(
	112	+ X_train: np.ndarray,
	113	+ y_train: np.ndarray,
	114	+ alphas: Optional[List[float]] = None,
	115	+ l1_ratios: Optional[List[float]] = None,
	116	+ cv: int = 5,
	117	+ random_state: int = 42,
	118	+) -> ElasticNetCV:
	119	+ """Train an ElasticNet with built-in cross-validated alpha selection.
	120	+
	121	+ Parameters
	122	+ ----------
	123	+ X_train, y_train : np.ndarray
	124	+ Training data.
	125	+ alphas : list[float] \| None
	126	+ Candidate regularisation strengths. Defaults to a log-spaced grid.
	127	+ l1_ratios : list[float] \| None
	128	+ L1/L2 mixing ratios to try. Defaults to ``[0.1, 0.5, 0.7, 0.9, 0.95, 1.0]``.
	129	+ cv : int
	130	+ Number of CV folds.
	131	+ random_state : int
	132	+ Seed for reproducibility.
	133	+
	134	+ Returns
	135	+ -------
	136	+ ElasticNetCV
	137	+ Fitted model.
	138	+ """
	139	+ if alphas is None:
	140	+ alphas = np.logspace(-4, 2, 50).tolist()
	141	+ if l1_ratios is None:
	142	+ l1_ratios = [0.1, 0.5, 0.7, 0.9, 0.95, 1.0]
	143	+
	144	+ model = ElasticNetCV(
	145	+ alphas=alphas,
	146	+ l1_ratio=l1_ratios,
	147	+ cv=cv,
	148	+ random_state=random_state,
	149	+ max_iter=10_000,
	150	+ n_jobs=-1,
	151	+ )
	152	+ model.fit(X_train, y_train)
	153	+ logger.info(
	154	+ "ElasticNet alpha=%.6f l1_ratio=%.2f",
	155	+ model.alpha_,
	156	+ model.l1_ratio_,
	157	+ )
	158	+ return model
	159	+
	160	+
	161	+def train_random_forest(
	162	+ X_train: np.ndarray,
	163	+ y_train: np.ndarray,
	164	+ n_estimators: int = 500,
	165	+ max_depth: Optional[int] = None,
	166	+ min_samples_leaf: int = 5,
	167	+ random_state: int = 42,
	168	+) -> RandomForestRegressor:
	169	+ """Train a Random Forest regressor.
	170	+
	171	+ Parameters
	172	+ ----------
	173	+ X_train, y_train : np.ndarray
	174	+ Training data.
	175	+ n_estimators : int
	176	+ Number of trees.
	177	+ max_depth : int \| None
	178	+ Maximum tree depth (``None`` = unlimited).
	179	+ min_samples_leaf : int
	180	+ Minimum samples per leaf node.
	181	+ random_state : int
	182	+ Seed for reproducibility.
	183	+
	184	+ Returns
	185	+ -------
	186	+ RandomForestRegressor
	187	+ Fitted model.
	188	+ """
	189	+ model = RandomForestRegressor(
	190	+ n_estimators=n_estimators,
	191	+ max_depth=max_depth,
	192	+ min_samples_leaf=min_samples_leaf,
	193	+ random_state=random_state,
	194	+ n_jobs=-1,
	195	+ )
	196	+ model.fit(X_train, y_train)
	197	+ logger.info("RandomForest n_estimators=%d fitted.", n_estimators)
	198	+ return model
	199	+
	200	+
	201	+def train_gradient_boosting(
	202	+ X_train: np.ndarray,
	203	+ y_train: np.ndarray,
	204	+ n_estimators: int = 500,
	205	+ learning_rate: float = 0.05,
	206	+ max_depth: int = 5,
	207	+ subsample: float = 0.8,
	208	+ random_state: int = 42,
	209	+) -> GradientBoostingRegressor:
	210	+ """Train a scikit-learn GradientBoostingRegressor.
	211	+
	212	+ Parameters
	213	+ ----------
	214	+ X_train, y_train : np.ndarray
	215	+ Training data.
	216	+ n_estimators : int
	217	+ Boosting rounds.
	218	+ learning_rate : float
	219	+ Shrinkage factor.
	220	+ max_depth : int
	221	+ Maximum tree depth per stage.
	222	+ subsample : float
	223	+ Row subsampling fraction.
	224	+ random_state : int
	225	+ Seed for reproducibility.
	226	+
	227	+ Returns
	228	+ -------
	229	+ GradientBoostingRegressor
	230	+ Fitted model.
	231	+ """
	232	+ model = GradientBoostingRegressor(
	233	+ n_estimators=n_estimators,
	234	+ learning_rate=learning_rate,
	235	+ max_depth=max_depth,
	236	+ subsample=subsample,
	237	+ random_state=random_state,
	238	+ )
	239	+ model.fit(X_train, y_train)
	240	+ logger.info("GradientBoosting fitted (n_estimators=%d).", n_estimators)
	241	+ return model
	242	+
	243	+
	244	+def train_xgboost(
	245	+ X_train: np.ndarray,
	246	+ y_train: np.ndarray,
	247	+ n_estimators: int = 500,
	248	+ learning_rate: float = 0.05,
	249	+ max_depth: int = 6,
	250	+ subsample: float = 0.8,
	251	+ colsample_bytree: float = 0.8,
	252	+ random_state: int = 42,
	253	+) -> Any:
	254	+ """Train an XGBRegressor. Falls back to sklearn GradientBoostingRegressor
	255	+ if ``xgboost`` is not installed.
	256	+
	257	+ Returns
	258	+ -------
	259	+ model
	260	+ Fitted regressor (XGBRegressor or GradientBoostingRegressor).
	261	+ """
	262	+ try:
	263	+ from xgboost import XGBRegressor
	264	+
	265	+ model = XGBRegressor(
	266	+ n_estimators=n_estimators,
	267	+ learning_rate=learning_rate,
	268	+ max_depth=max_depth,
	269	+ subsample=subsample,
	270	+ colsample_bytree=colsample_bytree,
	271	+ random_state=random_state,
	272	+ n_jobs=-1,
	273	+ verbosity=0,
	274	+ )
	275	+ model.fit(X_train, y_train)
	276	+ logger.info("XGBoost fitted (n_estimators=%d).", n_estimators)
	277	+ return model
	278	+ except ImportError:
	279	+ logger.warning("xgboost not installed — falling back to sklearn GBR.")
	280	+ return train_gradient_boosting(
	281	+ X_train,
	282	+ y_train,
	283	+ n_estimators=n_estimators,
	284	+ learning_rate=learning_rate,
	285	+ max_depth=max_depth,
	286	+ subsample=subsample,
	287	+ random_state=random_state,
	288	+ )
	289	+
	290	+
	291	+def train_lightgbm(
	292	+ X_train: np.ndarray,
	293	+ y_train: np.ndarray,
	294	+ n_estimators: int = 500,
	295	+ learning_rate: float = 0.05,
	296	+ num_leaves: int = 31,
	297	+ subsample: float = 0.8,
	298	+ colsample_bytree: float = 0.8,
	299	+ random_state: int = 42,
	300	+) -> Any:
	301	+ """Train an LGBMRegressor. Falls back to sklearn GBR if ``lightgbm`` is
	302	+ not installed.
	303	+
	304	+ Returns
	305	+ -------
	306	+ model
	307	+ Fitted regressor.
	308	+ """
	309	+ try:
	310	+ from lightgbm import LGBMRegressor
	311	+
	312	+ model = LGBMRegressor(
	313	+ n_estimators=n_estimators,
	314	+ learning_rate=learning_rate,
	315	+ num_leaves=num_leaves,
	316	+ subsample=subsample,
	317	+ colsample_bytree=colsample_bytree,
	318	+ random_state=random_state,
	319	+ n_jobs=-1,
	320	+ verbose=-1,
	321	+ )
	322	+ model.fit(X_train, y_train)
	323	+ logger.info("LightGBM fitted (n_estimators=%d).", n_estimators)
	324	+ return model
	325	+ except ImportError:
	326	+ logger.warning("lightgbm not installed — falling back to sklearn GBR.")
	327	+ return train_gradient_boosting(
	328	+ X_train,
	329	+ y_train,
	330	+ n_estimators=n_estimators,
	331	+ learning_rate=learning_rate,
	332	+ max_depth=5,
	333	+ subsample=subsample,
	334	+ random_state=random_state,
	335	+ )
	336	+
	337	+
	338	+def train_catboost(
	339	+ X_train: np.ndarray,
	340	+ y_train: np.ndarray,
	341	+ iterations: int = 500,
	342	+ learning_rate: float = 0.05,
	343	+ depth: int = 6,
	344	+ random_state: int = 42,
	345	+) -> Any:
	346	+ """Train a CatBoostRegressor. Falls back to sklearn GBR if ``catboost``
	347	+ is not installed.
	348	+
	349	+ Returns
	350	+ -------
	351	+ model
	352	+ Fitted regressor.
	353	+ """
	354	+ try:
	355	+ from catboost import CatBoostRegressor
	356	+
	357	+ model = CatBoostRegressor(
	358	+ iterations=iterations,
	359	+ learning_rate=learning_rate,
	360	+ depth=depth,
	361	+ random_state=random_state,
	362	+ verbose=0,
	363	+ )
	364	+ model.fit(X_train, y_train)
	365	+ logger.info("CatBoost fitted (iterations=%d).", iterations)
	366	+ return model
	367	+ except ImportError:
	368	+ logger.warning("catboost not installed — falling back to sklearn GBR.")
	369	+ return train_gradient_boosting(
	370	+ X_train,
	371	+ y_train,
	372	+ n_estimators=iterations,
	373	+ learning_rate=learning_rate,
	374	+ max_depth=depth,
	375	+ random_state=random_state,
	376	+ )
	377	+
	378	+
	379	+def train_gam(
	380	+ X_train: np.ndarray,
	381	+ y_train: np.ndarray,
	382	+ n_splines: int = 20,
	383	+ degree: int = 2,
	384	+) -> Any:
	385	+ """Train a Generalized Additive Model.
	386	+
	387	+ Attempts to use ``pygam.LinearGAM``; if unavailable, builds a polynomial-
	388	+ feature pipeline as a lightweight approximation.
	389	+
	390	+ Returns
	391	+ -------
	392	+ model
	393	+ Fitted GAM or polynomial pipeline.
	394	+ """
	395	+ try:
	396	+ from pygam import LinearGAM, s
	397	+
	398	+ # Build one spline term per feature.
	399	+ terms = s(0, n_splines=n_splines)
	400	+ for j in range(1, X_train.shape[1]):
	401	+ terms += s(j, n_splines=n_splines)
	402	+
	403	+ model = LinearGAM(terms)
	404	+ model.gridsearch(X_train, y_train, progress=False)
	405	+ logger.info("pygam LinearGAM fitted (n_splines=%d).", n_splines)
	406	+ return model
	407	+ except ImportError:
	408	+ logger.warning(
	409	+ "pygam not installed — approximating GAM with polynomial features (degree=%d).",
	410	+ degree,
	411	+ )
	412	+ pipe = Pipeline(
	413	+ [
	414	+ ("poly", PolynomialFeatures(degree=degree, interaction_only=True, include_bias=False)),
	415	+ ("scaler", StandardScaler()),
	416	+ (
	417	+ "enet",
	418	+ ElasticNetCV(
	419	+ l1_ratio=[0.5, 0.7, 0.9, 0.95, 1.0],
	420	+ cv=5,
	421	+ max_iter=10_000,
	422	+ n_jobs=-1,
	423	+ ),
	424	+ ),
	425	+ ]
	426	+ )
	427	+ pipe.fit(X_train, y_train)
	428	+ logger.info("Polynomial GAM approximation fitted (degree=%d).", degree)
	429	+ return pipe
	430	+
	431	+
	432	+# ---------------------------------------------------------------------------
	433	+# Train All Models
	434	+# ---------------------------------------------------------------------------
	435	+
	436	+
	437	+def train_all_models(
	438	+ X_train: np.ndarray,
	439	+ y_train: np.ndarray,
	440	+) -> Dict[str, Any]:
	441	+ """Train every available model and return a name -> fitted model dict.
	442	+
	443	+ Models that fail to train (e.g. convergence issues) are logged and
	444	+ skipped rather than raising.
	445	+
	446	+ Returns
	447	+ -------
	448	+ dict[str, model]
	449	+ Mapping of model name to fitted estimator.
	450	+ """
	451	+ trainers: Dict[str, Any] = {
	452	+ "ElasticNet": train_elastic_net,
	453	+ "RandomForest": train_random_forest,
	454	+ "GradientBoosting": train_gradient_boosting,
	455	+ "XGBoost": train_xgboost,
	456	+ "LightGBM": train_lightgbm,
	457	+ "CatBoost": train_catboost,
	458	+ "GAM": train_gam,
	459	+ }
	460	+
	461	+ models: Dict[str, Any] = {}
	462	+ for name, trainer in trainers.items():
	463	+ try:
	464	+ logger.info("Training %s ...", name)
	465	+ models[name] = trainer(X_train, y_train)
	466	+ except Exception:
	467	+ logger.exception("Failed to train %s — skipping.", name)
	468	+ return models
	469	+
	470	+
	471	+# ---------------------------------------------------------------------------
	472	+# Evaluation
	473	+# ---------------------------------------------------------------------------
	474	+
	475	+
	476	+def evaluate_model(
	477	+ model: Any,
	478	+ X_test: np.ndarray,
	479	+ y_test: np.ndarray,
	480	+) -> Dict[str, float]:
	481	+ """Compute regression metrics for a fitted model.
	482	+
	483	+ Returns
	484	+ -------
	485	+ dict
	486	+ Keys: ``R2``, ``RMSE``, ``MAE``, ``MAPE``.
	487	+ """
	488	+ y_pred = model.predict(X_test)
	489	+ r2 = r2_score(y_test, y_pred)
	490	+ rmse = float(np.sqrt(mean_squared_error(y_test, y_pred)))
	491	+ mae = mean_absolute_error(y_test, y_pred)
	492	+
	493	+ # Guard against zero prices when computing MAPE.
	494	+ with warnings.catch_warnings():
	495	+ warnings.simplefilter("ignore")
	496	+ mask = y_test != 0
	497	+ if mask.sum() > 0:
	498	+ mape = float(np.mean(np.abs((y_test[mask] - y_pred[mask]) / y_test[mask]))) * 100
	499	+ else:
	500	+ mape = np.nan
	501	+
	502	+ metrics = {"R2": r2, "RMSE": rmse, "MAE": mae, "MAPE": mape}
	503	+ logger.info(
	504	+ "Evaluation R2=%.4f RMSE=%.2f MAE=%.2f MAPE=%.2f%%",
	505	+ r2,
	506	+ rmse,
	507	+ mae,
	508	+ mape,
	509	+ )
	510	+ return metrics
	511	+
	512	+
	513	+def compare_all_models(
	514	+ models_dict: Dict[str, Any],
	515	+ X_test: np.ndarray,
	516	+ y_test: np.ndarray,
	517	+) -> pd.DataFrame:
	518	+ """Evaluate every model and return a comparison DataFrame sorted by R2.
	519	+
	520	+ Parameters
	521	+ ----------
	522	+ models_dict : dict[str, model]
	523	+ Output of :func:`train_all_models`.
	524	+ X_test, y_test : np.ndarray
	525	+ Held-out test data.
	526	+
	527	+ Returns
	528	+ -------
	529	+ pd.DataFrame
	530	+ Columns: ``Model``, ``R2``, ``RMSE``, ``MAE``, ``MAPE``.
	531	+ """
	532	+ rows: List[Dict[str, Any]] = []
	533	+ for name, model in models_dict.items():
	534	+ try:
	535	+ metrics = evaluate_model(model, X_test, y_test)
	536	+ rows.append({"Model": name, **metrics})
	537	+ except Exception:
	538	+ logger.exception("Evaluation failed for %s.", name)
	539	+
	540	+ comparison = pd.DataFrame(rows).sort_values("R2", ascending=False).reset_index(drop=True)
	541	+ logger.info("\n%s", comparison.to_string(index=False))
	542	+ return comparison
	543	+
	544	+
	545	+# ---------------------------------------------------------------------------
	546	+# Cross-Validation
	547	+# ---------------------------------------------------------------------------
	548	+
	549	+
	550	+def cross_validate_model(
	551	+ model: Any,
	552	+ X: np.ndarray,
	553	+ y: np.ndarray,
	554	+ k: int = 5,
	555	+ random_state: int = 42,
	556	+) -> Dict[str, float]:
	557	+ """K-fold cross-validation returning mean and std of R2, RMSE, and MAE.
	558	+
	559	+ Parameters
	560	+ ----------
	561	+ model : estimator
	562	+ An unfitted or fitted sklearn-compatible estimator. A clone is
	563	+ created internally, so the caller's model is not mutated.
	564	+ X, y : np.ndarray
	565	+ Full dataset (train + test).
	566	+ k : int
	567	+ Number of folds.
	568	+ random_state : int
	569	+ Seed for KFold split.
	570	+
	571	+ Returns
	572	+ -------
	573	+ dict
	574	+ Keys: ``R2_mean``, ``R2_std``, ``RMSE_mean``, ``RMSE_std``,
	575	+ ``MAE_mean``, ``MAE_std``.
	576	+ """
	577	+ from sklearn.base import clone
	578	+
	579	+ kf = KFold(n_splits=k, shuffle=True, random_state=random_state)
	580	+ r2_scores: List[float] = []
	581	+ rmse_scores: List[float] = []
	582	+ mae_scores: List[float] = []
	583	+
	584	+ for train_idx, val_idx in kf.split(X):
	585	+ X_tr, X_val = X[train_idx], X[val_idx]
	586	+ y_tr, y_val = y[train_idx], y[val_idx]
	587	+
	588	+ m = clone(model)
	589	+ m.fit(X_tr, y_tr)
	590	+ y_pred = m.predict(X_val)
	591	+
	592	+ r2_scores.append(r2_score(y_val, y_pred))
	593	+ rmse_scores.append(float(np.sqrt(mean_squared_error(y_val, y_pred))))
	594	+ mae_scores.append(mean_absolute_error(y_val, y_pred))
	595	+
	596	+ results = {
	597	+ "R2_mean": float(np.mean(r2_scores)),
	598	+ "R2_std": float(np.std(r2_scores)),
	599	+ "RMSE_mean": float(np.mean(rmse_scores)),
	600	+ "RMSE_std": float(np.std(rmse_scores)),
	601	+ "MAE_mean": float(np.mean(mae_scores)),
	602	+ "MAE_std": float(np.std(mae_scores)),
	603	+ }
	604	+ logger.info(
	605	+ "CV(%d) R2=%.4f +/- %.4f RMSE=%.2f +/- %.2f",
	606	+ k,
	607	+ results["R2_mean"],
	608	+ results["R2_std"],
	609	+ results["RMSE_mean"],
	610	+ results["RMSE_std"],
	611	+ )
	612	+ return results
	613	+
	614	+
	615	+# ---------------------------------------------------------------------------
	616	+# Feature Importance
	617	+# ---------------------------------------------------------------------------
	618	+
	619	+
	620	+def get_feature_importance(
	621	+ model: Any,
	622	+ feature_names: List[str],
	623	+) -> pd.DataFrame:
	624	+ """Extract feature importances from a fitted model.
	625	+
	626	+ Supports tree-based models (``feature_importances_``), linear models
	627	+ (``coef_``), and ``pygam`` (partial dependence variance).
	628	+
	629	+ Parameters
	630	+ ----------
	631	+ model : estimator
	632	+ Fitted model.
	633	+ feature_names : list[str]
	634	+ Feature column names.
	635	+
	636	+ Returns
	637	+ -------
	638	+ pd.DataFrame
	639	+ Columns: ``Feature``, ``Importance``, sorted descending.
	640	+ """
	641	+ importances: Optional[np.ndarray] = None
	642	+
	643	+ # Tree-based models (RF, GBR, XGB, LGBM, CatBoost).
	644	+ if hasattr(model, "feature_importances_"):
	645	+ importances = np.asarray(model.feature_importances_)
	646	+
	647	+ # Linear models (ElasticNet, Ridge, Lasso).
	648	+ elif hasattr(model, "coef_"):
	649	+ importances = np.abs(np.asarray(model.coef_))
	650	+
	651	+ # pygam — approximate importance via partial dependence variance.
	652	+ elif hasattr(model, "partial_dependence"):
	653	+ try:
	654	+ variances = []
	655	+ for j in range(len(feature_names)):
	656	+ XX = model.generate_X_grid(term=j)
	657	+ pdep, _ = model.partial_dependence(term=j, X=XX, width=0.95)
	658	+ variances.append(float(np.var(pdep)))
	659	+ importances = np.array(variances)
	660	+ except Exception:
	661	+ logger.warning("Could not extract importance from pygam model.")
	662	+
	663	+ # Pipeline fallback — try last step.
	664	+ elif hasattr(model, "named_steps"):
	665	+ last_step = list(model.named_steps.values())[-1]
	666	+ if hasattr(last_step, "coef_"):
	667	+ # Polynomial pipeline: importances don't map 1-to-1 to original
	668	+ # features. Aggregate absolute coefficients.
	669	+ poly = model.named_steps.get("poly")
	670	+ if poly is not None:
	671	+ coefs = np.abs(last_step.coef_)
	672	+ powers = poly.powers_
	673	+ agg = np.zeros(len(feature_names))
	674	+ for idx, power_vec in enumerate(powers):
	675	+ for feat_idx in np.nonzero(power_vec)[0]:
	676	+ if feat_idx < len(feature_names):
	677	+ agg[feat_idx] += coefs[idx] if idx < len(coefs) else 0.0
	678	+ importances = agg
	679	+ else:
	680	+ importances = np.abs(np.asarray(last_step.coef_))
	681	+
	682	+ if importances is None:
	683	+ logger.warning(
	684	+ "Cannot extract importance from model type %s.",
	685	+ type(model).__name__,
	686	+ )
	687	+ return pd.DataFrame(columns=["Feature", "Importance"])
	688	+
	689	+ # Truncate / pad to match feature_names length.
	690	+ if len(importances) > len(feature_names):
	691	+ importances = importances[: len(feature_names)]
	692	+ elif len(importances) < len(feature_names):
	693	+ importances = np.pad(importances, (0, len(feature_names) - len(importances)))
	694	+
	695	+ df_imp = (
	696	+ pd.DataFrame({"Feature": feature_names, "Importance": importances})
	697	+ .sort_values("Importance", ascending=False)
	698	+ .reset_index(drop=True)
	699	+ )
	700	+ return df_imp
	701	+
	702	+
	703	+# ---------------------------------------------------------------------------
	704	+# Hyperparameter Tuning
	705	+# ---------------------------------------------------------------------------
	706	+
	707	+
	708	+def tune_best_model(
	709	+ X_train: np.ndarray,
	710	+ y_train: np.ndarray,
	711	+ model_type: str = "xgboost",
	712	+ n_iter: int = 50,
	713	+ cv: int = 5,
	714	+ random_state: int = 42,
	715	+ scoring: str = "neg_root_mean_squared_error",
	716	+) -> Tuple[Any, Dict[str, Any]]:
	717	+ """Randomised hyperparameter search for a given model type.
	718	+
	719	+ Parameters
	720	+ ----------
	721	+ X_train, y_train : np.ndarray
	722	+ Training data.
	723	+ model_type : str
	724	+ One of ``'xgboost'``, ``'lightgbm'``, ``'catboost'``,
	725	+ ``'random_forest'``, ``'gradient_boosting'``.
	726	+ n_iter : int
	727	+ Number of parameter settings sampled.
	728	+ cv : int
	729	+ Cross-validation folds.
	730	+ random_state : int
	731	+ Seed for reproducibility.
	732	+ scoring : str
	733	+ Metric for ``RandomizedSearchCV``.
	734	+
	735	+ Returns
	736	+ -------
	737	+ best_model
	738	+ Refitted model with the best parameter set.
	739	+ best_params : dict
	740	+ Best parameters found.
	741	+ """
	742	+
	743	+ # --- estimator + param distributions -----------------------------------
	744	+ if model_type == "xgboost":
	745	+ try:
	746	+ from xgboost import XGBRegressor
	747	+
	748	+ estimator = XGBRegressor(random_state=random_state, n_jobs=-1, verbosity=0)
	749	+ except ImportError:
	750	+ logger.warning("xgboost unavailable — tuning sklearn GBR instead.")
	751	+ estimator = GradientBoostingRegressor(random_state=random_state)
	752	+ model_type = "gradient_boosting"
	753	+ elif model_type == "lightgbm":
	754	+ try:
	755	+ from lightgbm import LGBMRegressor
	756	+
	757	+ estimator = LGBMRegressor(random_state=random_state, n_jobs=-1, verbose=-1)
	758	+ except ImportError:
	759	+ logger.warning("lightgbm unavailable — tuning sklearn GBR instead.")
	760	+ estimator = GradientBoostingRegressor(random_state=random_state)
	761	+ model_type = "gradient_boosting"
	762	+ elif model_type == "catboost":
	763	+ try:
	764	+ from catboost import CatBoostRegressor
	765	+
	766	+ estimator = CatBoostRegressor(random_state=random_state, verbose=0)
	767	+ except ImportError:
	768	+ logger.warning("catboost unavailable — tuning sklearn GBR instead.")
	769	+ estimator = GradientBoostingRegressor(random_state=random_state)
	770	+ model_type = "gradient_boosting"
	771	+ elif model_type == "random_forest":
	772	+ estimator = RandomForestRegressor(random_state=random_state, n_jobs=-1)
	773	+ else:
	774	+ estimator = GradientBoostingRegressor(random_state=random_state)
	775	+ model_type = "gradient_boosting"
	776	+
	777	+ param_grids: Dict[str, Dict[str, Any]] = {
	778	+ "xgboost": {
	779	+ "n_estimators": [200, 500, 800, 1000],
	780	+ "learning_rate": [0.01, 0.03, 0.05, 0.1],
	781	+ "max_depth": [3, 4, 5, 6, 8],
	782	+ "subsample": [0.6, 0.7, 0.8, 0.9, 1.0],
	783	+ "colsample_bytree": [0.6, 0.7, 0.8, 0.9, 1.0],
	784	+ "min_child_weight": [1, 3, 5, 7],
	785	+ "reg_alpha": [0, 0.01, 0.1, 1],
	786	+ "reg_lambda": [0.5, 1, 2, 5],
	787	+ },
	788	+ "lightgbm": {
	789	+ "n_estimators": [200, 500, 800, 1000],
	790	+ "learning_rate": [0.01, 0.03, 0.05, 0.1],
	791	+ "num_leaves": [15, 31, 63, 127],
	792	+ "subsample": [0.6, 0.7, 0.8, 0.9, 1.0],
	793	+ "colsample_bytree": [0.6, 0.7, 0.8, 0.9, 1.0],
	794	+ "min_child_samples": [5, 10, 20, 50],
	795	+ "reg_alpha": [0, 0.01, 0.1, 1],
	796	+ "reg_lambda": [0.5, 1, 2, 5],
	797	+ },
	798	+ "catboost": {
	799	+ "iterations": [200, 500, 800, 1000],
	800	+ "learning_rate": [0.01, 0.03, 0.05, 0.1],
	801	+ "depth": [4, 6, 8, 10],
	802	+ "l2_leaf_reg": [1, 3, 5, 7, 9],
	803	+ "bagging_temperature": [0, 0.5, 1],
	804	+ },
	805	+ "random_forest": {
	806	+ "n_estimators": [200, 500, 800, 1000],
	807	+ "max_depth": [None, 10, 20, 30, 50],
	808	+ "min_samples_leaf": [1, 2, 5, 10],
	809	+ "min_samples_split": [2, 5, 10],
	810	+ "max_features": ["sqrt", "log2", 0.5, 0.8, None],
	811	+ },
	812	+ "gradient_boosting": {
	813	+ "n_estimators": [200, 500, 800, 1000],
	814	+ "learning_rate": [0.01, 0.03, 0.05, 0.1],
	815	+ "max_depth": [3, 4, 5, 6, 8],
	816	+ "subsample": [0.6, 0.7, 0.8, 0.9, 1.0],
	817	+ "min_samples_leaf": [1, 2, 5, 10],
	818	+ },
	819	+ }
	820	+
	821	+ param_dist = param_grids.get(model_type, param_grids["gradient_boosting"])
	822	+
	823	+ search = RandomizedSearchCV(
	824	+ estimator,
	825	+ param_distributions=param_dist,
	826	+ n_iter=n_iter,
	827	+ scoring=scoring,
	828	+ cv=cv,
	829	+ random_state=random_state,
	830	+ n_jobs=-1,
	831	+ verbose=0,
	832	+ )
	833	+ search.fit(X_train, y_train)
	834	+
	835	+ logger.info(
	836	+ "Best %s params: %s (score=%.4f)",
	837	+ model_type,
	838	+ search.best_params_,
	839	+ search.best_score_,
	840	+ )
	841	+ return search.best_estimator_, search.best_params_

added src/nlp_features.py +640 −0

		`@@ -0,0 +1,640 @@`
	1	+"""
	2	+NLP Feature Extraction for Airbnb Hedonic Pricing Analysis.
	3	+
	4	+Extracts linguistic, semantic, and structural features from listing text fields
	5	+(primarily 'name', but also 'description', 'neighborhood_overview', 'host_about'
	6	+when available in richer datasets).
	7	+
	8	+Supports bilingual (French/English) Airbnb listings common in Quebec markets.
	9	+
	10	+Functions
	11	+---------
	12	+extract_basic_text_features : Character-level and token-level text statistics.
	13	+extract_language_scores : Keyword-based thematic scoring (luxury, cozy, ...).
	14	+extract_property_info_from_name : Structured parsing of the Airbnb name template.
	15	+create_text_embeddings : TF-IDF or CountVectorizer with TruncatedSVD reduction.
	16	+find_price_associated_words : Correlation of TF-IDF terms with (log) price.
	17	+create_all_nlp_features : Master pipeline combining all of the above.
	18	+"""
	19	+
	20	+from __future__ import annotations
	21	+
	22	+import logging
	23	+import re
	24	+from typing import Dict, List, Optional, Tuple
	25	+
	26	+import numpy as np
	27	+import pandas as pd
	28	+from scipy import stats
	29	+from sklearn.decomposition import TruncatedSVD
	30	+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
	31	+
	32	+logger = logging.getLogger(__name__)
	33	+
	34	+# ---------------------------------------------------------------------------
	35	+# Keyword dictionaries (bilingual FR/EN)
	36	+# ---------------------------------------------------------------------------
	37	+
	38	+LANGUAGE_KEYWORDS: Dict[str, List[str]] = {
	39	+ "luxury_language_score": [
	40	+ "luxe", "luxury", "premium", "elegant", "chic", "spa",
	41	+ "designer", "upscale", "exclusive", "prestige", "haut de gamme",
	42	+ "somptueux", "raffiné", "opulent", "magnifique",
	43	+ ],
	44	+ "cozy_language_score": [
	45	+ "cozy", "cosy", "chaleureux", "douillet", "confortable",
	46	+ "warm", "charming", "intime", "accueillant", "nid",
	47	+ "mignon", "petit coin", "cocoon",
	48	+ ],
	49	+ "professional_tone_score": [
	50	+ "professional", "modern", "clean", "bright", "renovated",
	51	+ "new", "stylish", "design", "moderne", "lumineux",
	52	+ "rénové", "neuf", "propre", "contemporain", "minimaliste",
	53	+ ],
	54	+ "family_language_score": [
	55	+ "family", "famille", "kids", "enfants", "children",
	56	+ "spacious", "grand", "spacieux", "familial", "aire de jeux",
	57	+ "playground", "bébé", "baby",
	58	+ ],
	59	+ "business_language_score": [
	60	+ "business", "workspace", "bureau", "wifi", "downtown",
	61	+ "centre-ville", "corporate", "professionnel", "travail",
	62	+ "work", "desk", "office",
	63	+ ],
	64	+ "romantic_language_score": [
	65	+ "romantic", "romantique", "intimate", "couple",
	66	+ "retreat", "getaway", "escapade", "amoureux",
	67	+ "lune de miel", "honeymoon", "chandelle",
	68	+ ],
	69	+ "tourism_language_score": [
	70	+ "tourist", "touristique", "attractions", "view", "vue",
	71	+ "panoramic", "nature", "montagne", "lac", "ski",
	72	+ "mountain", "lake", "panoramique", "plein air",
	73	+ "randonnée", "hiking", "plage", "beach", "forêt", "forest",
	74	+ ],
	75	+}
	76	+
	77	+
	78	+# ---------------------------------------------------------------------------
	79	+# 1. Basic text features
	80	+# ---------------------------------------------------------------------------
	81	+
	82	+def extract_basic_text_features(df: pd.DataFrame) -> pd.DataFrame:
	83	+ """Extract character-level and token-level statistics from listing text.
	84	+
	85	+ Parameters
	86	+ ----------
	87	+ df : pd.DataFrame
	88	+ Must contain a ``name`` column. Optionally ``description``,
	89	+ ``neighborhood_overview``, and ``host_about``.
	90	+
	91	+ Returns
	92	+ -------
	93	+ pd.DataFrame
	94	+ Copy of df with appended feature columns:
	95	+ ``title_length``, ``title_word_count``, ``exclamation_count``,
	96	+ ``uppercase_ratio``, ``has_star_rating``, ``extracted_star_rating``.
	97	+ """
	98	+ logger.info("Extracting basic text features ...")
	99	+ result = df.copy()
	100	+
	101	+ name_series: pd.Series = result["name"].fillna("")
	102	+
	103	+ # Character count
	104	+ result["title_length"] = name_series.str.len()
	105	+
	106	+ # Word count (split on whitespace)
	107	+ result["title_word_count"] = name_series.str.split().str.len().fillna(0).astype(int)
	108	+
	109	+ # Exclamation marks
	110	+ result["exclamation_count"] = name_series.str.count("!")
	111	+
	112	+ # Upper-case ratio (letters only; avoid division by zero)
	113	+ alpha_chars = name_series.str.replace(r"[^A-Za-zÀ-ÿ]", "", regex=True)
	114	+ upper_chars = name_series.str.replace(r"[^A-ZÀ-Ý]", "", regex=True)
	115	+ alpha_len = alpha_chars.str.len().replace(0, np.nan)
	116	+ result["uppercase_ratio"] = (upper_chars.str.len() / alpha_len).fillna(0.0)
	117	+
	118	+ # Star rating detection
	119	+ result["has_star_rating"] = name_series.str.contains("★", regex=False)
	120	+
	121	+ # Numeric star rating extraction (e.g. ★4.94 or ★5.0)
	122	+ extracted = name_series.str.extract(r"★(\d+\.?\d*)", expand=False)
	123	+ result["extracted_star_rating"] = pd.to_numeric(extracted, errors="coerce")
	124	+
	125	+ # --- Optional: description-level features (if column exists) -----------
	126	+ for col in ("description", "neighborhood_overview", "host_about"):
	127	+ if col in result.columns:
	128	+ text = result[col].fillna("")
	129	+ result[f"{col}_length"] = text.str.len()
	130	+ result[f"{col}_word_count"] = text.str.split().str.len().fillna(0).astype(int)
	131	+ logger.info(" Added length/word_count for '%s'.", col)
	132	+
	133	+ logger.info("Basic text features extracted: %d rows.", len(result))
	134	+ return result
	135	+
	136	+
	137	+# ---------------------------------------------------------------------------
	138	+# 2. Language / keyword scores
	139	+# ---------------------------------------------------------------------------
	140	+
	141	+def _score_keywords(text: str, keywords: List[str]) -> int:
	142	+ """Count how many distinct keywords appear in text (case-insensitive)."""
	143	+ text_lower = text.lower()
	144	+ return sum(1 for kw in keywords if kw in text_lower)
	145	+
	146	+
	147	+def extract_language_scores(
	148	+ df: pd.DataFrame,
	149	+ text_columns: Optional[List[str]] = None,
	150	+) -> pd.DataFrame:
	151	+ """Score each listing on thematic keyword dimensions.
	152	+
	153	+ For every theme defined in ``LANGUAGE_KEYWORDS`` the score equals the
	154	+ number of matching keywords found in the concatenated text of all
	155	+ text_columns (case-insensitive substring matching).
	156	+
	157	+ Parameters
	158	+ ----------
	159	+ df : pd.DataFrame
	160	+ Input dataframe.
	161	+ text_columns : list[str] \| None
	162	+ Columns to scan. Defaults to ``['name']`` plus any of
	163	+ ``description``, ``neighborhood_overview``, ``host_about`` that
	164	+ exist in df.
	165	+
	166	+ Returns
	167	+ -------
	168	+ pd.DataFrame
	169	+ Copy with one new column per theme key in ``LANGUAGE_KEYWORDS``.
	170	+ """
	171	+ logger.info("Extracting language keyword scores ...")
	172	+ result = df.copy()
	173	+
	174	+ if text_columns is None:
	175	+ text_columns = ["name"]
	176	+ for optional in ("description", "neighborhood_overview", "host_about"):
	177	+ if optional in result.columns:
	178	+ text_columns.append(optional)
	179	+
	180	+ # Build a single concatenated text per row
	181	+ combined_text: pd.Series = (
	182	+ result[text_columns]
	183	+ .fillna("")
	184	+ .apply(lambda row: " ".join(row), axis=1)
	185	+ )
	186	+
	187	+ for score_name, keywords in LANGUAGE_KEYWORDS.items():
	188	+ result[score_name] = combined_text.apply(_score_keywords, keywords=keywords)
	189	+ logger.debug(" %s mean=%.3f", score_name, result[score_name].mean())
	190	+
	191	+ logger.info("Language scores computed for %d themes.", len(LANGUAGE_KEYWORDS))
	192	+ return result
	193	+
	194	+
	195	+# ---------------------------------------------------------------------------
	196	+# 3. Structured name parsing
	197	+# ---------------------------------------------------------------------------
	198	+
	199	+# Pre-compiled patterns for the Airbnb name template:
	200	+# "PropertyType in CityName · ★Rating · N bedroom(s) · N bed(s) · N bath(s)"
	201	+_RE_CITY = re.compile(
	202	+ r"^.+?\s+in\s+(.+?)(?:\s+·\|$)", re.IGNORECASE
	203	+)
	204	+_RE_BEDROOMS = re.compile(
	205	+ r"(\d+)\s+bedroom", re.IGNORECASE
	206	+)
	207	+_RE_STUDIO = re.compile(
	208	+ r"\bStudio\b", re.IGNORECASE
	209	+)
	210	+_RE_BEDS = re.compile(
	211	+ r"(\d+)\s+beds?\b", re.IGNORECASE
	212	+)
	213	+_RE_BATHS = re.compile(
	214	+ r"([\d.]+)\s+(?:shared\s+)?(?:private\s+)?bath", re.IGNORECASE
	215	+)
	216	+_RE_HALF_BATH = re.compile(
	217	+ r"\bHalf-bath\b", re.IGNORECASE
	218	+)
	219	+_RE_NEW = re.compile(
	220	+ r"★New\b", re.IGNORECASE
	221	+)
	222	+_RE_PROPERTY_TYPE = re.compile(
	223	+ r"^(.+?)\s+in\s+", re.IGNORECASE
	224	+)
	225	+
	226	+
	227	+def extract_property_info_from_name(df: pd.DataFrame) -> pd.DataFrame:
	228	+ """Parse the structured Airbnb ``name`` field into discrete features.
	229	+
	230	+ Parameters
	231	+ ----------
	232	+ df : pd.DataFrame
	233	+ Must contain column ``name``.
	234	+
	235	+ Returns
	236	+ -------
	237	+ pd.DataFrame
	238	+ Copy with columns: ``parsed_property_type``, ``parsed_city``,
	239	+ ``parsed_bedrooms``, ``parsed_beds``, ``parsed_baths``,
	240	+ ``is_studio``, ``is_new_listing``, ``has_shared_bath``,
	241	+ ``has_private_bath``.
	242	+ """
	243	+ logger.info("Parsing property info from name field ...")
	244	+ result = df.copy()
	245	+
	246	+ names: pd.Series = result["name"].fillna("")
	247	+
	248	+ # --- Parsed property type ------------------------------------------------
	249	+ result["parsed_property_type"] = names.str.extract(
	250	+ _RE_PROPERTY_TYPE, expand=False
	251	+ )
	252	+
	253	+ # --- City ----------------------------------------------------------------
	254	+ result["parsed_city"] = names.str.extract(_RE_CITY, expand=False)
	255	+
	256	+ # --- Bedrooms (0 for Studio) --------------------------------------------
	257	+ bedroom_match = names.str.extract(_RE_BEDROOMS, expand=False)
	258	+ is_studio = names.str.contains(_RE_STUDIO, regex=True)
	259	+ result["parsed_bedrooms"] = pd.to_numeric(bedroom_match, errors="coerce")
	260	+ result.loc[is_studio & result["parsed_bedrooms"].isna(), "parsed_bedrooms"] = 0
	261	+ result["is_studio"] = is_studio
	262	+
	263	+ # --- Beds ----------------------------------------------------------------
	264	+ beds_match = names.str.extract(_RE_BEDS, expand=False)
	265	+ result["parsed_beds"] = pd.to_numeric(beds_match, errors="coerce")
	266	+
	267	+ # --- Baths (handles "1.5 baths", "Half-bath", "shared bath") ------------
	268	+ baths_match = names.str.extract(_RE_BATHS, expand=False)
	269	+ result["parsed_baths"] = pd.to_numeric(baths_match, errors="coerce")
	270	+ # Handle "Half-bath" => 0.5
	271	+ half_bath_mask = names.str.contains(_RE_HALF_BATH, regex=True)
	272	+ result.loc[half_bath_mask & result["parsed_baths"].isna(), "parsed_baths"] = 0.5
	273	+
	274	+ # --- Qualifiers ----------------------------------------------------------
	275	+ result["is_new_listing"] = names.str.contains(_RE_NEW, regex=True)
	276	+ result["has_shared_bath"] = names.str.contains(r"shared\s+bath", case=False, regex=True)
	277	+ result["has_private_bath"] = names.str.contains(r"private\s+bath", case=False, regex=True)
	278	+
	279	+ logger.info("Name parsing complete: %d rows.", len(result))
	280	+ return result
	281	+
	282	+
	283	+# ---------------------------------------------------------------------------
	284	+# 4. Text embeddings (TF-IDF / CountVectorizer + TruncatedSVD)
	285	+# ---------------------------------------------------------------------------
	286	+
	287	+def create_text_embeddings(
	288	+ df: pd.DataFrame,
	289	+ method: str = "tfidf",
	290	+ n_components: int = 10,
	291	+ text_columns: Optional[List[str]] = None,
	292	+ max_features: int = 5000,
	293	+ ngram_range: Tuple[int, int] = (1, 2),
	294	+ random_state: int = 42,
	295	+) -> pd.DataFrame:
	296	+ """Create dense low-dimensional text embeddings via TF-IDF/Count + SVD.
	297	+
	298	+ Parameters
	299	+ ----------
	300	+ df : pd.DataFrame
	301	+ Input data.
	302	+ method : {'tfidf', 'count'}
	303	+ Vectoriser method.
	304	+ n_components : int
	305	+ Number of SVD components to retain.
	306	+ text_columns : list[str] \| None
	307	+ Text columns to concatenate. Defaults to ``['name']`` + optional
	308	+ ``description`` if present.
	309	+ max_features : int
	310	+ Maximum vocabulary size for the vectoriser.
	311	+ ngram_range : tuple[int, int]
	312	+ N-gram range passed to the vectoriser.
	313	+ random_state : int
	314	+ Seed for ``TruncatedSVD``.
	315	+
	316	+ Returns
	317	+ -------
	318	+ pd.DataFrame
	319	+ Copy with ``text_embed_0`` ... ``text_embed_{n_components-1}`` appended.
	320	+ """
	321	+ logger.info("Creating text embeddings (method=%s, n_components=%d) ...", method, n_components)
	322	+ result = df.copy()
	323	+
	324	+ if text_columns is None:
	325	+ text_columns = ["name"]
	326	+ if "description" in result.columns:
	327	+ text_columns.append("description")
	328	+
	329	+ # Concatenate and clean
	330	+ corpus: pd.Series = (
	331	+ result[text_columns]
	332	+ .fillna("")
	333	+ .apply(lambda row: " ".join(row), axis=1)
	334	+ )
	335	+ corpus = corpus.str.lower()
	336	+ corpus = corpus.str.replace(r"[^\w\sÀ-ÿ]", " ", regex=True) # strip punctuation
	337	+ corpus = corpus.str.replace(r"\s+", " ", regex=True).str.strip()
	338	+
	339	+ # Replace empty strings to avoid all-zero rows
	340	+ corpus = corpus.replace("", "empty_listing")
	341	+
	342	+ # Vectorise
	343	+ if method == "count":
	344	+ vectoriser = CountVectorizer(
	345	+ max_features=max_features,
	346	+ ngram_range=ngram_range,
	347	+ stop_words=None, # keep French words
	348	+ )
	349	+ else:
	350	+ vectoriser = TfidfVectorizer(
	351	+ max_features=max_features,
	352	+ ngram_range=ngram_range,
	353	+ stop_words=None,
	354	+ sublinear_tf=True,
	355	+ )
	356	+
	357	+ dtm = vectoriser.fit_transform(corpus)
	358	+ logger.info(" Vocabulary size: %d, document-term matrix shape: %s", len(vectoriser.vocabulary_), dtm.shape)
	359	+
	360	+ # Ensure n_components does not exceed matrix rank
	361	+ effective_components = min(n_components, dtm.shape[1] - 1, dtm.shape[0] - 1)
	362	+ if effective_components < n_components:
	363	+ logger.warning(
	364	+ "Reducing n_components from %d to %d (matrix rank constraint).",
	365	+ n_components, effective_components,
	366	+ )
	367	+
	368	+ if effective_components < 1:
	369	+ logger.warning("Not enough data for SVD; skipping embeddings.")
	370	+ return result
	371	+
	372	+ svd = TruncatedSVD(n_components=effective_components, random_state=random_state)
	373	+ embeddings = svd.fit_transform(dtm)
	374	+
	375	+ explained = svd.explained_variance_ratio_.sum()
	376	+ logger.info(" SVD explained variance: %.2f%%", explained * 100)
	377	+
	378	+ embed_cols = [f"text_embed_{i}" for i in range(effective_components)]
	379	+ embed_df = pd.DataFrame(embeddings, columns=embed_cols, index=result.index)
	380	+ result = pd.concat([result, embed_df], axis=1)
	381	+
	382	+ # Pad with zeros if we reduced components
	383	+ for i in range(effective_components, n_components):
	384	+ result[f"text_embed_{i}"] = 0.0
	385	+
	386	+ logger.info("Text embeddings added: %d components.", n_components)
	387	+ return result
	388	+
	389	+
	390	+# ---------------------------------------------------------------------------
	391	+# 5. Price-associated words
	392	+# ---------------------------------------------------------------------------
	393	+
	394	+def find_price_associated_words(
	395	+ df: pd.DataFrame,
	396	+ price_col: str = "log_price",
	397	+ top_n: int = 20,
	398	+ text_columns: Optional[List[str]] = None,
	399	+ max_features: int = 3000,
	400	+) -> pd.DataFrame:
	401	+ """Identify words most correlated with listing price.
	402	+
	403	+ Builds a TF-IDF matrix from listing text and computes Pearson
	404	+ correlation of each term column with the price variable.
	405	+
	406	+ Parameters
	407	+ ----------
	408	+ df : pd.DataFrame
	409	+ Must contain price_col and at least ``name``.
	410	+ price_col : str
	411	+ Column with (log-)price values. Rows with NaN are excluded.
	412	+ top_n : int
	413	+ Number of top positively and negatively correlated words.
	414	+ text_columns : list[str] \| None
	415	+ Text columns to concatenate.
	416	+ max_features : int
	417	+ Maximum vocabulary size.
	418	+
	419	+ Returns
	420	+ -------
	421	+ pd.DataFrame
	422	+ Columns: ``word``, ``correlation``, ``abs_correlation``,
	423	+ ``direction`` ('positive' \| 'negative').
	424	+ Sorted by absolute correlation descending.
	425	+ """
	426	+ logger.info("Finding price-associated words (price_col=%s, top_n=%d) ...", price_col, top_n)
	427	+
	428	+ if text_columns is None:
	429	+ text_columns = ["name"]
	430	+ if "description" in df.columns:
	431	+ text_columns.append("description")
	432	+
	433	+ # Work on rows with valid price
	434	+ if price_col not in df.columns:
	435	+ # Attempt to create log_price on the fly
	436	+ if "price_numeric" in df.columns:
	437	+ work = df.copy()
	438	+ work["log_price"] = np.log1p(pd.to_numeric(work["price_numeric"], errors="coerce"))
	439	+ price_col = "log_price"
	440	+ else:
	441	+ logger.error("Price column '%s' not found. Cannot compute word correlations.", price_col)
	442	+ return pd.DataFrame(columns=["word", "correlation", "abs_correlation", "direction"])
	443	+
	444	+ else:
	445	+ work = df.copy()
	446	+
	447	+ mask = work[price_col].notna()
	448	+ work = work.loc[mask].reset_index(drop=True)
	449	+
	450	+ if len(work) < 30:
	451	+ logger.warning("Too few rows with valid price (%d); skipping word-price correlation.", len(work))
	452	+ return pd.DataFrame(columns=["word", "correlation", "abs_correlation", "direction"])
	453	+
	454	+ # Concatenate text
	455	+ corpus = (
	456	+ work[text_columns]
	457	+ .fillna("")
	458	+ .apply(lambda row: " ".join(row), axis=1)
	459	+ .str.lower()
	460	+ .str.replace(r"[^\w\sÀ-ÿ]", " ", regex=True)
	461	+ .str.replace(r"\s+", " ", regex=True)
	462	+ .str.strip()
	463	+ )
	464	+
	465	+ vectoriser = TfidfVectorizer(
	466	+ max_features=max_features,
	467	+ ngram_range=(1, 2),
	468	+ stop_words=None,
	469	+ sublinear_tf=True,
	470	+ min_df=5,
	471	+ )
	472	+ dtm = vectoriser.fit_transform(corpus)
	473	+ feature_names = vectoriser.get_feature_names_out()
	474	+ price_values = work[price_col].values
	475	+
	476	+ # Compute correlations
	477	+ correlations = np.zeros(len(feature_names))
	478	+ dtm_dense = dtm.toarray()
	479	+ for i in range(len(feature_names)):
	480	+ col_vals = dtm_dense[:, i]
	481	+ if col_vals.std() == 0:
	482	+ correlations[i] = 0.0
	483	+ else:
	484	+ correlations[i], _ = stats.pearsonr(col_vals, price_values)
	485	+
	486	+ corr_df = pd.DataFrame({
	487	+ "word": feature_names,
	488	+ "correlation": correlations,
	489	+ })
	490	+ corr_df["abs_correlation"] = corr_df["correlation"].abs()
	491	+ corr_df["direction"] = np.where(corr_df["correlation"] >= 0, "positive", "negative")
	492	+
	493	+ # Select top positive and top negative
	494	+ top_positive = corr_df.nlargest(top_n, "correlation")
	495	+ top_negative = corr_df.nsmallest(top_n, "correlation")
	496	+ combined = (
	497	+ pd.concat([top_positive, top_negative])
	498	+ .drop_duplicates(subset=["word"])
	499	+ .sort_values("abs_correlation", ascending=False)
	500	+ .reset_index(drop=True)
	501	+ )
	502	+
	503	+ logger.info(
	504	+ "Top correlated word: '%s' (r=%.3f); bottom: '%s' (r=%.3f)",
	505	+ combined.iloc[0]["word"],
	506	+ combined.iloc[0]["correlation"],
	507	+ combined.iloc[-1]["word"],
	508	+ combined.iloc[-1]["correlation"],
	509	+ )
	510	+ return combined
	511	+
	512	+
	513	+# ---------------------------------------------------------------------------
	514	+# 6. Master function
	515	+# ---------------------------------------------------------------------------
	516	+
	517	+def create_all_nlp_features(
	518	+ df: pd.DataFrame,
	519	+ embedding_method: str = "tfidf",
	520	+ n_components: int = 10,
	521	+ compute_embeddings: bool = True,
	522	+ compute_price_words: bool = True,
	523	+ price_col: str = "log_price",
	524	+ top_n_words: int = 20,
	525	+) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:
	526	+ """Run the full NLP feature-extraction pipeline.
	527	+
	528	+ Parameters
	529	+ ----------
	530	+ df : pd.DataFrame
	531	+ Raw listing data (must have at least ``name``).
	532	+ embedding_method : {'tfidf', 'count'}
	533	+ Vectorisation method for embeddings.
	534	+ n_components : int
	535	+ Number of SVD dimensions.
	536	+ compute_embeddings : bool
	537	+ Whether to compute text embeddings.
	538	+ compute_price_words : bool
	539	+ Whether to compute price-associated word analysis.
	540	+ price_col : str
	541	+ Price column for word-price correlation.
	542	+ top_n_words : int
	543	+ Number of top words per direction.
	544	+
	545	+ Returns
	546	+ -------
	547	+ tuple[pd.DataFrame, pd.DataFrame \| None]
	548	+ - Enriched DataFrame with all NLP features.
	549	+ - Price-word correlation DataFrame (or ``None`` if skipped).
	550	+ """
	551	+ logger.info("=" * 60)
	552	+ logger.info("NLP FEATURE EXTRACTION PIPELINE")
	553	+ logger.info("=" * 60)
	554	+ logger.info("Input shape: %s", df.shape)
	555	+
	556	+ if "name" not in df.columns:
	557	+ logger.error("Column 'name' not found. Returning input unchanged.")
	558	+ return df.copy(), None
	559	+
	560	+ # --- Step 1: basic text features ----------------------------------------
	561	+ enriched = extract_basic_text_features(df)
	562	+
	563	+ # --- Step 2: language keyword scores ------------------------------------
	564	+ enriched = extract_language_scores(enriched)
	565	+
	566	+ # --- Step 3: structured name parsing ------------------------------------
	567	+ enriched = extract_property_info_from_name(enriched)
	568	+
	569	+ # --- Step 4: text embeddings --------------------------------------------
	570	+ if compute_embeddings:
	571	+ enriched = create_text_embeddings(
	572	+ enriched,
	573	+ method=embedding_method,
	574	+ n_components=n_components,
	575	+ )
	576	+
	577	+ # --- Step 5: price-associated words -------------------------------------
	578	+ price_words: Optional[pd.DataFrame] = None
	579	+ if compute_price_words:
	580	+ price_words = find_price_associated_words(
	581	+ enriched,
	582	+ price_col=price_col,
	583	+ top_n=top_n_words,
	584	+ )
	585	+
	586	+ # --- Summary ------------------------------------------------------------
	587	+ new_cols = [c for c in enriched.columns if c not in df.columns]
	588	+ logger.info("NLP pipeline complete. %d new features added.", len(new_cols))
	589	+ logger.info("New columns: %s", new_cols)
	590	+ logger.info("Output shape: %s", enriched.shape)
	591	+
	592	+ return enriched, price_words
	593	+
	594	+
	595	+# ---------------------------------------------------------------------------
	596	+# CLI quick-test
	597	+# ---------------------------------------------------------------------------
	598	+
	599	+if __name__ == "__main__":
	600	+ import sys
	601	+
	602	+ logging.basicConfig(
	603	+ level=logging.INFO,
	604	+ format="%(asctime)s %(levelname)-8s %(message)s",
	605	+ stream=sys.stdout,
	606	+ )
	607	+
	608	+ sample_path = "cleaned_dataset_sample.csv"
	609	+ logger.info("Loading sample data from '%s' ...", sample_path)
	610	+ data = pd.read_csv(sample_path)
	611	+ logger.info("Loaded %d rows, %d columns.", *data.shape)
	612	+
	613	+ enriched_df, price_word_df = create_all_nlp_features(data)
	614	+
	615	+ print("\n--- Enriched DataFrame (first 5 rows, NLP columns) ---")
	616	+ nlp_cols = [
	617	+ "title_length", "title_word_count", "exclamation_count",
	618	+ "uppercase_ratio", "has_star_rating", "extracted_star_rating",
	619	+ "luxury_language_score", "cozy_language_score",
	620	+ "professional_tone_score", "family_language_score",
	621	+ "business_language_score", "romantic_language_score",
	622	+ "tourism_language_score",
	623	+ "parsed_property_type", "parsed_city",
	624	+ "parsed_bedrooms", "parsed_beds", "parsed_baths",
	625	+ "is_studio", "is_new_listing",
	626	+ "has_shared_bath", "has_private_bath",
	627	+ ]
	628	+ existing_nlp_cols = [c for c in nlp_cols if c in enriched_df.columns]
	629	+ print(enriched_df[existing_nlp_cols].head().to_string())
	630	+
	631	+ embed_cols = [c for c in enriched_df.columns if c.startswith("text_embed_")]
	632	+ if embed_cols:
	633	+ print(f"\n--- Text Embedding columns ({len(embed_cols)}) ---")
	634	+ print(enriched_df[embed_cols].describe().to_string())
	635	+
	636	+ if price_word_df is not None and not price_word_df.empty:
	637	+ print("\n--- Top Price-Associated Words ---")
	638	+ print(price_word_df.to_string(index=False))
	639	+ else:
	640	+ print("\n(No price-word correlations computed.)")

added src/quantile_models.py +243 −0

		`@@ -0,0 +1,243 @@`
	1	+"""Quantile regression models for heterogeneous price effect analysis."""
	2	+
	3	+import logging
	4	+from pathlib import Path
	5	+from typing import Dict, List, Optional, Sequence, Tuple
	6	+
	7	+import numpy as np
	8	+import pandas as pd
	9	+import statsmodels.api as sm
	10	+from statsmodels.regression.quantile_regression import QuantReg
	11	+
	12	+logger = logging.getLogger(__name__)
	13	+
	14	+DEFAULT_TAUS: List[float] = [0.10, 0.25, 0.50, 0.75, 0.90]
	15	+
	16	+
	17	+def _available_cols(df: pd.DataFrame, requested: List[str]) -> List[str]:
	18	+ """Return the subset of requested columns present in df."""
	19	+ present = [c for c in requested if c in df.columns]
	20	+ missing = set(requested) - set(present)
	21	+ if missing:
	22	+ logger.warning("Missing columns (skipped): %s", sorted(missing))
	23	+ return present
	24	+
	25	+
	26	+def _prepare_data(
	27	+ df: pd.DataFrame,
	28	+ y_col: str,
	29	+ x_cols: List[str],
	30	+) -> Tuple[pd.Series, pd.DataFrame]:
	31	+ """Drop NaN rows, add constant, return (y, X)."""
	32	+ cols = _available_cols(df, x_cols)
	33	+ if not cols:
	34	+ raise ValueError("No valid feature columns found in the DataFrame.")
	35	+ subset = df[[y_col] + cols].dropna()
	36	+ if subset.empty:
	37	+ raise ValueError("No observations remaining after dropping NaN rows.")
	38	+ y = subset[y_col].astype(float)
	39	+ X = sm.add_constant(subset[cols].astype(float))
	40	+ return y, X
	41	+
	42	+
	43	+# ---------------------------------------------------------------------------
	44	+# Single quantile estimation
	45	+# ---------------------------------------------------------------------------
	46	+
	47	+def estimate_quantile(
	48	+ df: pd.DataFrame,
	49	+ y_col: str,
	50	+ x_cols: List[str],
	51	+ tau: float,
	52	+ max_iter: int = 10_000,
	53	+) -> sm.regression.quantile_regression.QuantRegResults:
	54	+ """Single quantile regression at quantile tau."""
	55	+ if not 0 < tau < 1:
	56	+ raise ValueError(f"tau must be in (0, 1), got {tau}")
	57	+ y, X = _prepare_data(df, y_col, x_cols)
	58	+ model = QuantReg(y, X)
	59	+ results = model.fit(q=tau, max_iter=max_iter)
	60	+ logger.info(
	61	+ "Quantile regression (tau=%.2f): pseudo-R2=%.4f, n=%d",
	62	+ tau, results.prsquared, int(results.nobs),
	63	+ )
	64	+ return results
	65	+
	66	+
	67	+# ---------------------------------------------------------------------------
	68	+# Quantile sequence
	69	+# ---------------------------------------------------------------------------
	70	+
	71	+def estimate_quantile_sequence(
	72	+ df: pd.DataFrame,
	73	+ y_col: str,
	74	+ x_cols: List[str],
	75	+ taus: Optional[List[float]] = None,
	76	+ max_iter: int = 10_000,
	77	+) -> Dict[float, sm.regression.quantile_regression.QuantRegResults]:
	78	+ """Estimate quantile regressions at multiple quantiles."""
	79	+ if taus is None:
	80	+ taus = DEFAULT_TAUS
	81	+ results: Dict[float, sm.regression.quantile_regression.QuantRegResults] = {}
	82	+ for tau in sorted(taus):
	83	+ try:
	84	+ res = estimate_quantile(df, y_col, x_cols, tau, max_iter=max_iter)
	85	+ results[tau] = res
	86	+ except Exception as exc:
	87	+ logger.error("Quantile tau=%.2f failed: %s", tau, exc)
	88	+ logger.info("Estimated %d / %d quantile regressions.", len(results), len(taus))
	89	+ return results
	90	+
	91	+
	92	+# ---------------------------------------------------------------------------
	93	+# Coefficient comparison across quantiles
	94	+# ---------------------------------------------------------------------------
	95	+
	96	+def compare_quantile_coefficients(
	97	+ results_dict: Dict[float, sm.regression.quantile_regression.QuantRegResults],
	98	+) -> pd.DataFrame:
	99	+ """Extract coefficients and CIs across quantiles into a long DataFrame."""
	100	+ records = []
	101	+ for tau, res in sorted(results_dict.items()):
	102	+ ci = res.conf_int()
	103	+ for var in res.params.index:
	104	+ records.append({
	105	+ "tau": tau,
	106	+ "variable": var,
	107	+ "coefficient": res.params[var],
	108	+ "std_error": res.bse[var],
	109	+ "p_value": res.pvalues[var],
	110	+ "ci_lower": ci.loc[var, 0],
	111	+ "ci_upper": ci.loc[var, 1],
	112	+ })
	113	+ coef_df = pd.DataFrame(records)
	114	+ logger.info(
	115	+ "Coefficient comparison: %d variables x %d quantiles.",
	116	+ coef_df["variable"].nunique(), coef_df["tau"].nunique(),
	117	+ )
	118	+ return coef_df
	119	+
	120	+
	121	+# ---------------------------------------------------------------------------
	122	+# Plotting
	123	+# ---------------------------------------------------------------------------
	124	+
	125	+def plot_quantile_coefficients(
	126	+ coef_df: pd.DataFrame,
	127	+ variables: List[str],
	128	+ save_path: Optional[str] = None,
	129	+ figsize: Tuple[int, int] = (14, 4),
	130	+ ols_results: Optional[object] = None,
	131	+) -> None:
	132	+ """Plot coefficient evolution across quantiles with confidence bands."""
	133	+ try:
	134	+ import matplotlib.pyplot as plt
	135	+ except ImportError:
	136	+ logger.error("matplotlib is required for plotting.")
	137	+ return
	138	+
	139	+ n_vars = len(variables)
	140	+ if n_vars == 0:
	141	+ logger.warning("No variables specified for plotting.")
	142	+ return
	143	+
	144	+ n_cols = min(3, n_vars)
	145	+ n_rows = int(np.ceil(n_vars / n_cols))
	146	+ fig, axes = plt.subplots(n_rows, n_cols, figsize=(figsize[0], figsize[1] * n_rows))
	147	+ if n_vars == 1:
	148	+ axes = np.array([axes])
	149	+ axes = np.atleast_1d(axes).flatten()
	150	+
	151	+ for idx, var in enumerate(variables):
	152	+ ax = axes[idx]
	153	+ sub = coef_df[coef_df["variable"] == var].sort_values("tau")
	154	+ if sub.empty:
	155	+ ax.set_title(f"{var} (no data)")
	156	+ continue
	157	+
	158	+ taus = sub["tau"].values
	159	+ coefs = sub["coefficient"].values
	160	+ ci_lo = sub["ci_lower"].values
	161	+ ci_hi = sub["ci_upper"].values
	162	+
	163	+ ax.plot(taus, coefs, "o-", color="steelblue", linewidth=2, label="QR coef")
	164	+ ax.fill_between(taus, ci_lo, ci_hi, alpha=0.2, color="steelblue", label="95% CI")
	165	+
	166	+ if ols_results is not None and var in ols_results.params.index:
	167	+ ols_b = ols_results.params[var]
	168	+ ax.axhline(ols_b, color="firebrick", linestyle="--", linewidth=1, label="OLS")
	169	+
	170	+ ax.axhline(0, color="grey", linestyle=":", linewidth=0.5)
	171	+ ax.set_xlabel("Quantile (tau)")
	172	+ ax.set_ylabel("Coefficient")
	173	+ ax.set_title(var)
	174	+ ax.legend(fontsize=7)
	175	+
	176	+ # Hide unused axes
	177	+ for idx in range(n_vars, len(axes)):
	178	+ axes[idx].set_visible(False)
	179	+
	180	+ fig.tight_layout()
	181	+ if save_path:
	182	+ Path(save_path).parent.mkdir(parents=True, exist_ok=True)
	183	+ fig.savefig(save_path, dpi=200, bbox_inches="tight")
	184	+ logger.info("Quantile coefficient plot saved to %s", save_path)
	185	+ plt.close(fig)
	186	+
	187	+
	188	+# ---------------------------------------------------------------------------
	189	+# Segment effects summary
	190	+# ---------------------------------------------------------------------------
	191	+
	192	+def get_segment_effects(
	193	+ results_dict: Dict[float, sm.regression.quantile_regression.QuantRegResults],
	194	+) -> pd.DataFrame:
	195	+ """Summarize how effects differ across price segments (budget vs. luxury)."""
	196	+ coef_df = compare_quantile_coefficients(results_dict)
	197	+
	198	+ segments = {
	199	+ "budget (q10)": 0.10,
	200	+ "lower-mid (q25)": 0.25,
	201	+ "median (q50)": 0.50,
	202	+ "upper-mid (q75)": 0.75,
	203	+ "luxury (q90)": 0.90,
	204	+ }
	205	+
	206	+ available_taus = set(coef_df["tau"].unique())
	207	+ records = []
	208	+ variables = [v for v in coef_df["variable"].unique() if v != "const"]
	209	+
	210	+ for var in variables:
	211	+ var_data = coef_df[coef_df["variable"] == var]
	212	+ row: Dict[str, object] = {"variable": var}
	213	+
	214	+ for seg_name, tau in segments.items():
	215	+ if tau in available_taus:
	216	+ match = var_data[var_data["tau"] == tau]
	217	+ if not match.empty:
	218	+ b = match["coefficient"].values[0]
	219	+ p = match["p_value"].values[0]
	220	+ star = "*" if p < 0.001 else ("" if p < 0.01 else ("*" if p < 0.05 else ""))
	221	+ row[seg_name] = f"{b:.4f}{star}"
	222	+ else:
	223	+ row[seg_name] = ""
	224	+ else:
	225	+ row[seg_name] = ""
	226	+
	227	+ # Heterogeneity indicator: range of coefficients
	228	+ coefs = var_data["coefficient"].values
	229	+ if len(coefs) >= 2:
	230	+ row["coef_range"] = round(float(np.max(coefs) - np.min(coefs)), 4)
	231	+ row["heterogeneity"] = (
	232	+ "high" if row["coef_range"] > 0.1
	233	+ else ("moderate" if row["coef_range"] > 0.03 else "low")
	234	+ )
	235	+ else:
	236	+ row["coef_range"] = np.nan
	237	+ row["heterogeneity"] = "n/a"
	238	+
	239	+ records.append(row)
	240	+
	241	+ summary = pd.DataFrame(records)
	242	+ logger.info("Segment effects summarised for %d variables.", len(summary))
	243	+ return summary

added src/report.py +445 −0

		`@@ -0,0 +1,445 @@`
	1	+"""
	2	+report.py -- Price decomposition and reporting for hedonic pricing analysis.
	3	+
	4	+Provides functions to decompose predicted prices into interpretable
	5	+components (structural, amenity, host, review, location, text, spatial),
	6	+identify the strongest positive and negative price drivers for individual
	7	+listings, and orchestrate the full output-generation pipeline (tables,
	8	+figures, decomposition summaries).
	9	+"""
	10	+
	11	+from __future__ import annotations
	12	+
	13	+import logging
	14	+import time
	15	+from pathlib import Path
	16	+from typing import Any, Dict, List, Optional, Tuple
	17	+
	18	+import numpy as np
	19	+import pandas as pd
	20	+
	21	+from src import config
	22	+
	23	+logger = logging.getLogger(__name__)
	24	+
	25	+# ---------------------------------------------------------------------------
	26	+# 1. Single-listing price decomposition
	27	+# ---------------------------------------------------------------------------
	28	+
	29	+def explain_listing_price(
	30	+ model: Any,
	31	+ df: pd.DataFrame,
	32	+ listing_idx: int,
	33	+ feature_groups: Dict[str, List[str]],
	34	+ *,
	35	+ n_top: int = 10,
	36	+) -> Dict[str, Any]:
	37	+ """Decompose a single listing's predicted price into component contributions.
	38	+
	39	+ Parameters
	40	+ ----------
	41	+ model : fitted statsmodels-style regression result
	42	+ Must expose ``.params`` (Series of coefficients) and, when the model
	43	+ includes an intercept, a ``const`` / ``Intercept`` key.
	44	+ df : pd.DataFrame
	45	+ The design matrix used for estimation (same columns as model.params).
	46	+ listing_idx : int
	47	+ Row position (iloc index) of the listing to explain.
	48	+ feature_groups : dict[str, list[str]]
	49	+ Mapping of group name -> list of feature column names. Expected
	50	+ keys: ``"structural"``, ``"amenity"``, ``"host"``, ``"review"``,
	51	+ ``"location"``, ``"text"``, ``"spatial"``. Missing keys are
	52	+ treated as empty.
	53	+ n_top : int
	54	+ Number of top positive / negative drivers to return.
	55	+
	56	+ Returns
	57	+ -------
	58	+ dict with keys:
	59	+ actual_price, predicted_price, prediction_error,
	60	+ base_component, property_component, amenity_component,
	61	+ host_component, review_component, location_component,
	62	+ text_component, spatial_component,
	63	+ top_positive_drivers, top_negative_drivers
	64	+ """
	65	+ row = df.iloc[listing_idx]
	66	+ params = model.params
	67	+
	68	+ # Determine which features actually exist in both the model and the data
	69	+ model_features = set(params.index)
	70	+ data_features = set(df.columns)
	71	+ common_features = model_features & data_features
	72	+
	73	+ # --- intercept / base component ---
	74	+ intercept_keys = {"const", "Intercept", "intercept"}
	75	+ intercept_name = intercept_keys & model_features
	76	+ base_component = float(params[intercept_name.pop()]) if intercept_name else 0.0
	77	+
	78	+ # --- compute individual feature contributions ---
	79	+ contributions: Dict[str, float] = {}
	80	+ for feat in common_features:
	81	+ val = row.get(feat, 0.0)
	82	+ if pd.isna(val):
	83	+ val = 0.0
	84	+ contributions[feat] = float(params[feat]) * float(val)
	85	+
	86	+ # --- aggregate by group ---
	87	+ group_aliases = {
	88	+ "structural": "property_component",
	89	+ "amenity": "amenity_component",
	90	+ "host": "host_component",
	91	+ "review": "review_component",
	92	+ "location": "location_component",
	93	+ "text": "text_component",
	94	+ "spatial": "spatial_component",
	95	+ }
	96	+
	97	+ component_sums: Dict[str, float] = {}
	98	+ assigned_features: set = set()
	99	+
	100	+ for group_key, component_name in group_aliases.items():
	101	+ group_feats = feature_groups.get(group_key, [])
	102	+ total = 0.0
	103	+ for feat in group_feats:
	104	+ if feat in contributions:
	105	+ total += contributions[feat]
	106	+ assigned_features.add(feat)
	107	+ component_sums[component_name] = total
	108	+
	109	+ # Features present in the model but not assigned to any group are folded
	110	+ # into property_component (structural catch-all).
	111	+ for feat, contrib in contributions.items():
	112	+ if feat not in assigned_features:
	113	+ component_sums["property_component"] = (
	114	+ component_sums.get("property_component", 0.0) + contrib
	115	+ )
	116	+
	117	+ # --- predicted price ---
	118	+ predicted_price = base_component + sum(contributions.values())
	119	+
	120	+ # --- actual price ---
	121	+ # Try common dependent-variable column names
	122	+ actual_price = np.nan
	123	+ for dep_col in ("log_price", "price_numeric", "price"):
	124	+ if dep_col in df.columns:
	125	+ actual_price = float(row[dep_col])
	126	+ break
	127	+
	128	+ prediction_error = actual_price - predicted_price
	129	+
	130	+ # --- top drivers ---
	131	+ sorted_contribs = sorted(contributions.items(), key=lambda x: x[1], reverse=True)
	132	+ top_positive = [(f, c) for f, c in sorted_contribs if c > 0][:n_top]
	133	+ top_negative = [(f, c) for f, c in sorted_contribs if c < 0][-n_top:]
	134	+ # Sort negatives by magnitude (most negative first)
	135	+ top_negative = sorted(top_negative, key=lambda x: x[1])
	136	+
	137	+ return {
	138	+ "actual_price": actual_price,
	139	+ "predicted_price": predicted_price,
	140	+ "prediction_error": prediction_error,
	141	+ "base_component": base_component,
	142	+ **component_sums,
	143	+ "top_positive_drivers": top_positive,
	144	+ "top_negative_drivers": top_negative,
	145	+ }
	146	+
	147	+
	148	+# ---------------------------------------------------------------------------
	149	+# 2. Full-dataset decomposition
	150	+# ---------------------------------------------------------------------------
	151	+
	152	+def decompose_all_prices(
	153	+ model: Any,
	154	+ df: pd.DataFrame,
	155	+ feature_groups: Dict[str, List[str]],
	156	+) -> pd.DataFrame:
	157	+ """Apply :func:`explain_listing_price` to every row and return a DataFrame.
	158	+
	159	+ Each row corresponds to a listing; columns include all scalar components
	160	+ produced by the single-listing function. The ``top_positive_drivers``
	161	+ and ``top_negative_drivers`` lists are excluded from the DataFrame (use
	162	+ :func:`explain_listing_price` for individual inspection).
	163	+
	164	+ Parameters
	165	+ ----------
	166	+ model : fitted statsmodels result
	167	+ df : pd.DataFrame
	168	+ feature_groups : dict[str, list[str]]
	169	+
	170	+ Returns
	171	+ -------
	172	+ pd.DataFrame
	173	+ """
	174	+ logger.info("Decomposing prices for %d listings ...", len(df))
	175	+ t0 = time.perf_counter()
	176	+
	177	+ records: List[Dict[str, float]] = []
	178	+ scalar_keys = [
	179	+ "actual_price", "predicted_price", "prediction_error",
	180	+ "base_component", "property_component", "amenity_component",
	181	+ "host_component", "review_component", "location_component",
	182	+ "text_component", "spatial_component",
	183	+ ]
	184	+
	185	+ for idx in range(len(df)):
	186	+ try:
	187	+ expl = explain_listing_price(model, df, idx, feature_groups)
	188	+ records.append({k: expl.get(k, 0.0) for k in scalar_keys})
	189	+ except Exception:
	190	+ logger.debug("Decomposition failed for listing idx=%d", idx, exc_info=True)
	191	+ records.append({k: np.nan for k in scalar_keys})
	192	+
	193	+ elapsed = time.perf_counter() - t0
	194	+ logger.info("Price decomposition complete in %.1f s", elapsed)
	195	+ return pd.DataFrame(records, index=df.index)
	196	+
	197	+
	198	+# ---------------------------------------------------------------------------
	199	+# 3. Decomposition summary statistics
	200	+# ---------------------------------------------------------------------------
	201	+
	202	+def generate_decomposition_summary(decomp_df: pd.DataFrame) -> pd.DataFrame:
	203	+ """Compute summary statistics for each price component.
	204	+
	205	+ Parameters
	206	+ ----------
	207	+ decomp_df : pd.DataFrame
	208	+ Output of :func:`decompose_all_prices`.
	209	+
	210	+ Returns
	211	+ -------
	212	+ pd.DataFrame
	213	+ Index = component names, columns = mean, median, std, min, max.
	214	+ """
	215	+ component_cols = [
	216	+ c for c in decomp_df.columns
	217	+ if c.endswith("_component") or c in ("predicted_price", "actual_price", "prediction_error")
	218	+ ]
	219	+ summary = decomp_df[component_cols].agg(["mean", "median", "std", "min", "max"]).T
	220	+ summary.columns = ["mean", "median", "std", "min", "max"]
	221	+ summary = summary.round(4)
	222	+ logger.info("Decomposition summary:\n%s", summary.to_string())
	223	+ return summary
	224	+
	225	+
	226	+# ---------------------------------------------------------------------------
	227	+# 4. Pretty-print a single listing explanation
	228	+# ---------------------------------------------------------------------------
	229	+
	230	+def format_decomposition_report(listing_explanation: Dict[str, Any]) -> str:
	231	+ """Return a human-readable string summarising a listing's price decomposition.
	232	+
	233	+ Parameters
	234	+ ----------
	235	+ listing_explanation : dict
	236	+ Output of :func:`explain_listing_price`.
	237	+
	238	+ Returns
	239	+ -------
	240	+ str
	241	+ """
	242	+ e = listing_explanation
	243	+ lines: List[str] = []
	244	+ sep = "-" * 64
	245	+
	246	+ lines.append(sep)
	247	+ lines.append("LISTING PRICE DECOMPOSITION REPORT")
	248	+ lines.append(sep)
	249	+ lines.append(f" Actual price : {e.get('actual_price', np.nan):>12.4f}")
	250	+ lines.append(f" Predicted price : {e.get('predicted_price', np.nan):>12.4f}")
	251	+ lines.append(f" Prediction error : {e.get('prediction_error', np.nan):>12.4f}")
	252	+ lines.append("")
	253	+ lines.append(" COMPONENTS")
	254	+ lines.append(f" Base (intercept) : {e.get('base_component', 0.0):>12.4f}")
	255	+ lines.append(f" Property : {e.get('property_component', 0.0):>12.4f}")
	256	+ lines.append(f" Amenity : {e.get('amenity_component', 0.0):>12.4f}")
	257	+ lines.append(f" Host : {e.get('host_component', 0.0):>12.4f}")
	258	+ lines.append(f" Review : {e.get('review_component', 0.0):>12.4f}")
	259	+ lines.append(f" Location : {e.get('location_component', 0.0):>12.4f}")
	260	+ lines.append(f" Text / NLP : {e.get('text_component', 0.0):>12.4f}")
	261	+ lines.append(f" Spatial : {e.get('spatial_component', 0.0):>12.4f}")
	262	+ lines.append("")
	263	+
	264	+ # Top positive drivers
	265	+ pos = e.get("top_positive_drivers", [])
	266	+ if pos:
	267	+ lines.append(" TOP POSITIVE DRIVERS")
	268	+ for feat, contrib in pos:
	269	+ lines.append(f" {feat:<40s} +{contrib:>10.4f}")
	270	+ lines.append("")
	271	+
	272	+ # Top negative drivers
	273	+ neg = e.get("top_negative_drivers", [])
	274	+ if neg:
	275	+ lines.append(" TOP NEGATIVE DRIVERS")
	276	+ for feat, contrib in neg:
	277	+ lines.append(f" {feat:<40s} {contrib:>10.4f}")
	278	+ lines.append("")
	279	+
	280	+ lines.append(sep)
	281	+ return "\n".join(lines)
	282	+
	283	+
	284	+# ---------------------------------------------------------------------------
	285	+# 5. Master report / output generation pipeline
	286	+# ---------------------------------------------------------------------------
	287	+
	288	+def generate_full_report(
	289	+ df: pd.DataFrame,
	290	+ models_dict: Dict[str, Any],
	291	+ ml_models_dict: Dict[str, Any],
	292	+ spatial_results: Dict[str, Any],
	293	+ feature_groups: Dict[str, List[str]],
	294	+ output_dir: str \| Path = config.OUTPUTS_DIR,
	295	+) -> Dict[str, Any]:
	296	+ """Orchestrate full output generation: tables, figures, decomposition.
	297	+
	298	+ Calls the ``tables`` and ``figures`` modules to produce every artefact
	299	+ required for the research paper, then runs the price-decomposition
	300	+ pipeline and saves the summary.
	301	+
	302	+ Parameters
	303	+ ----------
	304	+ df : pd.DataFrame
	305	+ Feature-engineered dataset.
	306	+ models_dict : dict[str, Any]
	307	+ Mapping of model name -> fitted hedonic / quantile result.
	308	+ ml_models_dict : dict[str, Any]
	309	+ Mapping of model name -> fitted ML model or results dict.
	310	+ spatial_results : dict[str, Any]
	311	+ Output from the spatial-models module.
	312	+ feature_groups : dict[str, list[str]]
	313	+ Feature group registry.
	314	+ output_dir : str or Path
	315	+ Root output directory (tables/ and figures/ subdirectories are created).
	316	+
	317	+ Returns
	318	+ -------
	319	+ dict with keys: ``decomp_df``, ``decomp_summary``, ``tables_generated``,
	320	+ ``figures_generated``, ``elapsed_seconds``.
	321	+ """
	322	+ t0 = time.perf_counter()
	323	+ output_dir = Path(output_dir)
	324	+ tables_dir = output_dir / "tables"
	325	+ figures_dir = output_dir / "figures"
	326	+ tables_dir.mkdir(parents=True, exist_ok=True)
	327	+ figures_dir.mkdir(parents=True, exist_ok=True)
	328	+
	329	+ report_artifacts: Dict[str, Any] = {
	330	+ "tables_generated": [],
	331	+ "figures_generated": [],
	332	+ }
	333	+
	334	+ # ---- Tables ----
	335	+ logger.info("Generating tables ...")
	336	+ try:
	337	+ from src import tables as tbl_mod
	338	+
	339	+ table_funcs = [
	340	+ ("descriptive_stats", lambda: tbl_mod.generate_descriptive_stats_table(df, tables_dir)),
	341	+ ("correlation_matrix", lambda: tbl_mod.generate_correlation_table(df, tables_dir)),
	342	+ ("hedonic_results", lambda: tbl_mod.generate_hedonic_results_table(models_dict, tables_dir)),
	343	+ ("quantile_results", lambda: tbl_mod.generate_quantile_results_table(models_dict, tables_dir)),
	344	+ ("ml_comparison", lambda: tbl_mod.generate_ml_comparison_table(ml_models_dict, tables_dir)),
	345	+ ("spatial_results", lambda: tbl_mod.generate_spatial_results_table(spatial_results, tables_dir)),
	346	+ ]
	347	+
	348	+ for name, func in table_funcs:
	349	+ try:
	350	+ func()
	351	+ report_artifacts["tables_generated"].append(name)
	352	+ logger.info(" Table '%s' generated.", name)
	353	+ except Exception:
	354	+ logger.warning(" Table '%s' failed.", name, exc_info=True)
	355	+
	356	+ except ImportError:
	357	+ logger.warning("tables module not available; skipping table generation.")
	358	+ except Exception:
	359	+ logger.warning("Table generation failed.", exc_info=True)
	360	+
	361	+ # ---- Figures ----
	362	+ logger.info("Generating figures ...")
	363	+ try:
	364	+ from src import figures as fig_mod
	365	+
	366	+ figure_funcs = [
	367	+ ("price_distribution", lambda: fig_mod.plot_price_distribution(df, figures_dir)),
	368	+ ("correlation_heatmap", lambda: fig_mod.plot_correlation_heatmap(df, figures_dir)),
	369	+ ("coefficient_plot", lambda: fig_mod.plot_coefficient_comparison(models_dict, figures_dir)),
	370	+ ("quantile_plot", lambda: fig_mod.plot_quantile_coefficients(models_dict, figures_dir)),
	371	+ ("spatial_map", lambda: fig_mod.plot_spatial_map(df, spatial_results, figures_dir)),
	372	+ ("residual_diagnostics", lambda: fig_mod.plot_residual_diagnostics(models_dict, df, figures_dir)),
	373	+ ("shap_summary", lambda: fig_mod.plot_shap_summary(ml_models_dict, df, figures_dir)),
	374	+ ]
	375	+
	376	+ for name, func in figure_funcs:
	377	+ try:
	378	+ func()
	379	+ report_artifacts["figures_generated"].append(name)
	380	+ logger.info(" Figure '%s' generated.", name)
	381	+ except Exception:
	382	+ logger.warning(" Figure '%s' failed.", name, exc_info=True)
	383	+
	384	+ except ImportError:
	385	+ logger.warning("figures module not available; skipping figure generation.")
	386	+ except Exception:
	387	+ logger.warning("Figure generation failed.", exc_info=True)
	388	+
	389	+ # ---- Price decomposition ----
	390	+ logger.info("Running price decomposition ...")
	391	+ decomp_df = pd.DataFrame()
	392	+ decomp_summary = pd.DataFrame()
	393	+
	394	+ # Pick the best available hedonic model for decomposition
	395	+ preferred_model_keys = [
	396	+ "ols_semilog", "ols_log", "ols_linear",
	397	+ "robust_huber", "robust_bisquare",
	398	+ ]
	399	+ decomp_model = None
	400	+ decomp_model_name = None
	401	+ for key in preferred_model_keys:
	402	+ if key in models_dict and models_dict[key] is not None:
	403	+ decomp_model = models_dict[key]
	404	+ decomp_model_name = key
	405	+ break
	406	+
	407	+ if decomp_model is not None:
	408	+ try:
	409	+ logger.info("Using model '%s' for price decomposition.", decomp_model_name)
	410	+ decomp_df = decompose_all_prices(decomp_model, df, feature_groups)
	411	+ decomp_summary = generate_decomposition_summary(decomp_df)
	412	+
	413	+ # Save artefacts
	414	+ decomp_csv = output_dir / "tables" / "price_decomposition.csv"
	415	+ decomp_df.to_csv(decomp_csv, index=False)
	416	+ logger.info("Decomposition saved to %s", decomp_csv)
	417	+
	418	+ summary_csv = output_dir / "tables" / "decomposition_summary.csv"
	419	+ decomp_summary.to_csv(summary_csv)
	420	+ logger.info("Summary saved to %s", summary_csv)
	421	+
	422	+ # Pretty-print a sample listing
	423	+ if len(decomp_df) > 0:
	424	+ sample_idx = 0
	425	+ sample_expl = explain_listing_price(
	426	+ decomp_model, df, sample_idx, feature_groups,
	427	+ )
	428	+ logger.info(
	429	+ "Sample decomposition:\n%s",
	430	+ format_decomposition_report(sample_expl),
	431	+ )
	432	+ except Exception:
	433	+ logger.warning("Price decomposition failed.", exc_info=True)
	434	+ else:
	435	+ logger.warning(
	436	+ "No suitable hedonic model found for decomposition (tried: %s).",
	437	+ ", ".join(preferred_model_keys),
	438	+ )
	439	+
	440	+ elapsed = time.perf_counter() - t0
	441	+ report_artifacts["decomp_df"] = decomp_df
	442	+ report_artifacts["decomp_summary"] = decomp_summary
	443	+ report_artifacts["elapsed_seconds"] = elapsed
	444	+ logger.info("Full report generation complete in %.1f s", elapsed)
	445	+ return report_artifacts

added src/robustness.py +675 −0

		`@@ -0,0 +1,675 @@`
	1	+"""
	2	+robustness.py - Robustness checks for hedonic pricing analysis.
	3	+
	4	+Implements a battery of specification tests following best practice in
	5	+applied micro-economics:
	6	+ - Outlier treatment (winsorisation, trimming, log, median regression)
	7	+ - Sub-sample regressions by room type and price segment
	8	+ - Progressive location control inclusion
	9	+ - Host-type stratification
	10	+ - Summary formatting for publication tables
	11	+
	12	+All functions return structured dicts that can be passed directly to
	13	+``tables.robustness_table()`` and ``tables.regression_comparison_table()``.
	14	+
	15	+Author: Simon-Pierre Boucher
	16	+Date: 2026-05-20
	17	+"""
	18	+
	19	+from __future__ import annotations
	20	+
	21	+import logging
	22	+from typing import Any, Dict, List, Optional, Tuple
	23	+
	24	+import numpy as np
	25	+import pandas as pd
	26	+
	27	+logger = logging.getLogger(__name__)
	28	+
	29	+
	30	+# ---------------------------------------------------------------------------
	31	+# Internal helpers
	32	+# ---------------------------------------------------------------------------
	33	+
	34	+def _safe_ols(
	35	+ y: pd.Series,
	36	+ X: pd.DataFrame,
	37	+ add_constant: bool = True,
	38	+) -> Any:
	39	+ """Run OLS and return results; raises on failure."""
	40	+ import statsmodels.api as sm
	41	+
	42	+ if add_constant:
	43	+ X = sm.add_constant(X, has_constant="skip")
	44	+ model = sm.OLS(y, X, missing="drop")
	45	+ return model.fit(cov_type="HC1")
	46	+
	47	+
	48	+def _result_to_dict(res: Any, label: str = "") -> Dict[str, Any]:
	49	+ """Convert a statsmodels result to a flat dict for comparison tables."""
	50	+ out: Dict[str, Any] = {
	51	+ "N": int(res.nobs),
	52	+ "R2": round(res.rsquared, 4),
	53	+ "Adj_R2": round(res.rsquared_adj, 4),
	54	+ "AIC": round(res.aic, 1),
	55	+ "BIC": round(res.bic, 1),
	56	+ }
	57	+ for v in res.params.index:
	58	+ out[v] = {
	59	+ "coef": round(float(res.params[v]), 6),
	60	+ "se": round(float(res.bse[v]), 6),
	61	+ "pvalue": round(float(res.pvalues[v]), 6),
	62	+ }
	63	+ return out
	64	+
	65	+
	66	+def _winsorize_series(s: pd.Series, limits: Tuple[float, float] = (0.01, 0.01)) -> pd.Series:
	67	+ """Winsorize a series at the given lower/upper percentile limits."""
	68	+ from scipy.stats.mstats import winsorize as _winz
	69	+ return pd.Series(_winz(s.dropna(), limits=limits), index=s.dropna().index)
	70	+
	71	+
	72	+def _trim_series(s: pd.Series, lower: float = 0.01, upper: float = 0.99) -> pd.Series:
	73	+ """Trim observations outside the specified quantile range."""
	74	+ lo = s.quantile(lower)
	75	+ hi = s.quantile(upper)
	76	+ mask = (s >= lo) & (s <= hi)
	77	+ return s[mask]
	78	+
	79	+
	80	+# ---------------------------------------------------------------------------
	81	+# 1. Winsorization / outlier treatment
	82	+# ---------------------------------------------------------------------------
	83	+
	84	+def robustness_winsorization(
	85	+ df: pd.DataFrame,
	86	+ y_col: str,
	87	+ x_cols: List[str],
	88	+ limits: Tuple[float, float] = (0.01, 0.01),
	89	+ trim_quantiles: Tuple[float, float] = (0.01, 0.99),
	90	+) -> Dict[str, Dict[str, Any]]:
	91	+ """
	92	+ Compare regressions under different outlier treatments.
	93	+
	94	+ Specifications:
	95	+ 1. Raw OLS
	96	+ 2. Winsorized dependent variable (1st / 99th)
	97	+ 3. Trimmed sample
	98	+ 4. Log-transformed dependent variable
	99	+ 5. Median (LAD) regression
	100	+
	101	+ Parameters
	102	+ ----------
	103	+ df : pd.DataFrame
	104	+ y_col : str
	105	+ Dependent variable (price).
	106	+ x_cols : list[str]
	107	+ Independent variables.
	108	+ limits : tuple
	109	+ Winsorization percentile limits.
	110	+ trim_quantiles : tuple
	111	+ Lower and upper quantile bounds for trimming.
	112	+
	113	+ Returns
	114	+ -------
	115	+ dict
	116	+ ``{spec_name: results_dict}``
	117	+ """
	118	+ import statsmodels.api as sm
	119	+
	120	+ logger.info("Robustness: winsorization / outlier treatment checks.")
	121	+ results: Dict[str, Dict[str, Any]] = {}
	122	+ sub = df.dropna(subset=[y_col] + x_cols).copy()
	123	+ X = sub[x_cols]
	124	+ y = sub[y_col]
	125	+
	126	+ # 1. Raw OLS
	127	+ try:
	128	+ res_raw = _safe_ols(y, X)
	129	+ results["Raw OLS"] = _result_to_dict(res_raw)
	130	+ except Exception as e:
	131	+ logger.error("Raw OLS failed: %s", e)
	132	+
	133	+ # 2. Winsorized
	134	+ try:
	135	+ y_wins = _winsorize_series(y, limits=limits)
	136	+ # Align X with winsorized y index
	137	+ X_wins = X.loc[y_wins.index]
	138	+ res_wins = _safe_ols(y_wins, X_wins)
	139	+ results["Winsorized (1%/99%)"] = _result_to_dict(res_wins)
	140	+ except Exception as e:
	141	+ logger.error("Winsorized OLS failed: %s", e)
	142	+
	143	+ # 3. Trimmed
	144	+ try:
	145	+ lo = y.quantile(trim_quantiles[0])
	146	+ hi = y.quantile(trim_quantiles[1])
	147	+ mask = (y >= lo) & (y <= hi)
	148	+ res_trim = _safe_ols(y[mask], X.loc[mask])
	149	+ results["Trimmed (1%-99%)"] = _result_to_dict(res_trim)
	150	+ except Exception as e:
	151	+ logger.error("Trimmed OLS failed: %s", e)
	152	+
	153	+ # 4. Log-transformed
	154	+ try:
	155	+ pos_mask = y > 0
	156	+ y_log = np.log(y[pos_mask])
	157	+ res_log = _safe_ols(y_log, X.loc[pos_mask])
	158	+ results["Log(price) OLS"] = _result_to_dict(res_log)
	159	+ except Exception as e:
	160	+ logger.error("Log OLS failed: %s", e)
	161	+
	162	+ # 5. Median regression (LAD)
	163	+ try:
	164	+ X_const = sm.add_constant(X, has_constant="skip")
	165	+ model_med = sm.QuantReg(y, X_const)
	166	+ res_med = model_med.fit(q=0.5)
	167	+ med_dict: Dict[str, Any] = {
	168	+ "N": int(res_med.nobs),
	169	+ "R2": np.nan, # pseudo R2 not directly comparable
	170	+ "Adj_R2": np.nan,
	171	+ }
	172	+ # Pseudo R2 for quantile regression
	173	+ try:
	174	+ med_dict["Pseudo_R2"] = round(float(1 - res_med.prsquared), 4)
	175	+ except Exception:
	176	+ pass
	177	+ for v in res_med.params.index:
	178	+ med_dict[v] = {
	179	+ "coef": round(float(res_med.params[v]), 6),
	180	+ "se": round(float(res_med.bse[v]), 6),
	181	+ "pvalue": round(float(res_med.pvalues[v]), 6),
	182	+ }
	183	+ results["Median Regression"] = med_dict
	184	+ except Exception as e:
	185	+ logger.error("Median regression failed: %s", e)
	186	+
	187	+ logger.info("Winsorization robustness: %d/%d specs succeeded.",
	188	+ len(results), 5)
	189	+ return results
	190	+
	191	+
	192	+# ---------------------------------------------------------------------------
	193	+# 2. By room type
	194	+# ---------------------------------------------------------------------------
	195	+
	196	+def robustness_by_room_type(
	197	+ df: pd.DataFrame,
	198	+ y_col: str,
	199	+ x_cols: List[str],
	200	+ room_col: str = "property_type",
	201	+ min_obs: int = 50,
	202	+) -> Dict[str, Dict[str, Any]]:
	203	+ """
	204	+ Separate regressions for each room/property type.
	205	+
	206	+ Parameters
	207	+ ----------
	208	+ df : pd.DataFrame
	209	+ y_col : str
	210	+ x_cols : list[str]
	211	+ room_col : str
	212	+ Column identifying room/property type.
	213	+ min_obs : int
	214	+ Minimum observations to run a regression for a given type.
	215	+
	216	+ Returns
	217	+ -------
	218	+ dict
	219	+ ``{room_type: results_dict}``
	220	+ """
	221	+ logger.info("Robustness: sub-sample by room type (%s).", room_col)
	222	+ sub = df.dropna(subset=[y_col] + x_cols + [room_col]).copy()
	223	+ results: Dict[str, Dict[str, Any]] = {}
	224	+
	225	+ # Full sample first
	226	+ try:
	227	+ res_full = _safe_ols(sub[y_col], sub[x_cols])
	228	+ results["Full Sample"] = _result_to_dict(res_full)
	229	+ except Exception as e:
	230	+ logger.error("Full sample OLS failed: %s", e)
	231	+
	232	+ for rtype, grp in sub.groupby(room_col, observed=True):
	233	+ if len(grp) < min_obs:
	234	+ logger.warning("Skipping %s (n=%d < %d).", rtype, len(grp), min_obs)
	235	+ continue
	236	+ # Remove x_cols that have zero variance in this sub-sample
	237	+ x_valid = [c for c in x_cols if grp[c].nunique() > 1]
	238	+ if not x_valid:
	239	+ logger.warning("Skipping %s: no valid regressors.", rtype)
	240	+ continue
	241	+ try:
	242	+ res = _safe_ols(grp[y_col], grp[x_valid])
	243	+ results[str(rtype)] = _result_to_dict(res)
	244	+ except Exception as e:
	245	+ logger.error("OLS for %s failed: %s", rtype, e)
	246	+
	247	+ logger.info("Room-type robustness: %d sub-samples estimated.", len(results))
	248	+ return results
	249	+
	250	+
	251	+# ---------------------------------------------------------------------------
	252	+# 3. By price segment
	253	+# ---------------------------------------------------------------------------
	254	+
	255	+def robustness_by_price_segment(
	256	+ df: pd.DataFrame,
	257	+ y_col: str,
	258	+ x_cols: List[str],
	259	+ segments: Optional[Dict[str, Tuple[float, float]]] = None,
	260	+ price_category_col: Optional[str] = "price_category",
	261	+ min_obs: int = 50,
	262	+) -> Dict[str, Dict[str, Any]]:
	263	+ """
	264	+ Separate regressions for budget, moderate, premium, luxury segments.
	265	+
	266	+ Parameters
	267	+ ----------
	268	+ df : pd.DataFrame
	269	+ y_col : str
	270	+ x_cols : list[str]
	271	+ segments : dict, optional
	272	+ ``{segment_name: (lower_bound, upper_bound)}``.
	273	+ If None, quartile-based segments are used.
	274	+ price_category_col : str, optional
	275	+ If present in df, use this column instead of numeric cuts.
	276	+ min_obs : int
	277	+ Minimum observations per segment.
	278	+
	279	+ Returns
	280	+ -------
	281	+ dict
	282	+ """
	283	+ logger.info("Robustness: sub-sample by price segment.")
	284	+ sub = df.dropna(subset=[y_col] + x_cols).copy()
	285	+ results: Dict[str, Dict[str, Any]] = {}
	286	+
	287	+ # Full sample
	288	+ try:
	289	+ res_full = _safe_ols(sub[y_col], sub[x_cols])
	290	+ results["Full Sample"] = _result_to_dict(res_full)
	291	+ except Exception as e:
	292	+ logger.error("Full sample OLS failed: %s", e)
	293	+
	294	+ if price_category_col and price_category_col in sub.columns:
	295	+ for seg, grp in sub.groupby(price_category_col, observed=True):
	296	+ if len(grp) < min_obs:
	297	+ continue
	298	+ x_valid = [c for c in x_cols if grp[c].nunique() > 1]
	299	+ if not x_valid:
	300	+ continue
	301	+ try:
	302	+ res = _safe_ols(grp[y_col], grp[x_valid])
	303	+ results[str(seg)] = _result_to_dict(res)
	304	+ except Exception as e:
	305	+ logger.error("Segment %s failed: %s", seg, e)
	306	+ else:
	307	+ if segments is None:
	308	+ q25, q50, q75 = sub[y_col].quantile([0.25, 0.50, 0.75])
	309	+ segments = {
	310	+ "Budget (Q1)": (sub[y_col].min(), q25),
	311	+ "Moderate (Q2)": (q25, q50),
	312	+ "Premium (Q3)": (q50, q75),
	313	+ "Luxury (Q4)": (q75, sub[y_col].max()),
	314	+ }
	315	+
	316	+ for seg_name, (lo, hi) in segments.items():
	317	+ grp = sub[(sub[y_col] >= lo) & (sub[y_col] <= hi)]
	318	+ if len(grp) < min_obs:
	319	+ logger.warning("Skipping %s (n=%d).", seg_name, len(grp))
	320	+ continue
	321	+ x_valid = [c for c in x_cols if grp[c].nunique() > 1]
	322	+ if not x_valid:
	323	+ continue
	324	+ try:
	325	+ res = _safe_ols(grp[y_col], grp[x_valid])
	326	+ results[seg_name] = _result_to_dict(res)
	327	+ except Exception as e:
	328	+ logger.error("Segment %s failed: %s", seg_name, e)
	329	+
	330	+ logger.info("Price-segment robustness: %d segments estimated.", len(results))
	331	+ return results
	332	+
	333	+
	334	+# ---------------------------------------------------------------------------
	335	+# 4. Location controls
	336	+# ---------------------------------------------------------------------------
	337	+
	338	+def robustness_location_controls(
	339	+ df: pd.DataFrame,
	340	+ y_col: str,
	341	+ x_cols: List[str],
	342	+ geo_col: str = "city",
	343	+ lat_col: str = "lat",
	344	+ lon_col: str = "long",
	345	+ n_clusters: int = 20,
	346	+) -> Dict[str, Dict[str, Any]]:
	347	+ """
	348	+ Compare specifications with progressively richer location controls.
	349	+
	350	+ Specifications:
	351	+ 1. No location controls
	352	+ 2. Neighbourhood / city fixed effects
	353	+ 3. Lat/long coordinates (linear)
	354	+ 4. Spatial clusters (k-means on coordinates)
	355	+ 5. Spatial lag of dependent variable (if possible)
	356	+
	357	+ Parameters
	358	+ ----------
	359	+ df : pd.DataFrame
	360	+ y_col : str
	361	+ x_cols : list[str]
	362	+ geo_col : str
	363	+ Geographic unit for fixed effects.
	364	+ lat_col, lon_col : str
	365	+ Coordinate columns.
	366	+ n_clusters : int
	367	+ Number of spatial clusters for k-means.
	368	+
	369	+ Returns
	370	+ -------
	371	+ dict
	372	+ """
	373	+ import statsmodels.api as sm
	374	+
	375	+ logger.info("Robustness: location control specifications.")
	376	+ sub = df.dropna(subset=[y_col] + x_cols).copy()
	377	+ results: Dict[str, Dict[str, Any]] = {}
	378	+
	379	+ # 1. No location controls
	380	+ try:
	381	+ res = _safe_ols(sub[y_col], sub[x_cols])
	382	+ results["No location"] = _result_to_dict(res)
	383	+ except Exception as e:
	384	+ logger.error("No-location OLS failed: %s", e)
	385	+
	386	+ # 2. Neighbourhood / city FE
	387	+ if geo_col in sub.columns:
	388	+ try:
	389	+ dummies = pd.get_dummies(sub[geo_col], prefix="fe", drop_first=True,
	390	+ dtype=float)
	391	+ # Limit to top categories to avoid singular matrix
	392	+ top_cats = sub[geo_col].value_counts().head(30).index
	393	+ dummies = dummies[[c for c in dummies.columns
	394	+ if any(cat in c for cat in top_cats.astype(str))]]
	395	+ X_fe = pd.concat([sub[x_cols], dummies], axis=1)
	396	+ res = _safe_ols(sub[y_col], X_fe)
	397	+ d = _result_to_dict(res)
	398	+ d["FE_count"] = len(dummies.columns)
	399	+ results[f"{geo_col} FE"] = d
	400	+ except Exception as e:
	401	+ logger.error("FE OLS failed: %s", e)
	402	+
	403	+ # 3. Lat/long coordinates
	404	+ coord_cols = [c for c in [lat_col, lon_col] if c in sub.columns]
	405	+ if len(coord_cols) == 2:
	406	+ try:
	407	+ sub_coord = sub.dropna(subset=coord_cols)
	408	+ X_coord = pd.concat(
	409	+ [sub_coord[x_cols], sub_coord[coord_cols]], axis=1)
	410	+ res = _safe_ols(sub_coord[y_col], X_coord)
	411	+ results["Coordinates"] = _result_to_dict(res)
	412	+ except Exception as e:
	413	+ logger.error("Coordinates OLS failed: %s", e)
	414	+
	415	+ # 4. Spatial clusters
	416	+ try:
	417	+ from sklearn.cluster import KMeans
	418	+ sub_clust = sub.dropna(subset=coord_cols).copy()
	419	+ coords = sub_clust[coord_cols].values
	420	+ km = KMeans(n_clusters=min(n_clusters, len(sub_clust) // 10),
	421	+ random_state=42, n_init=10)
	422	+ sub_clust["spatial_cluster"] = km.fit_predict(coords)
	423	+ cl_dummies = pd.get_dummies(sub_clust["spatial_cluster"],
	424	+ prefix="cl", drop_first=True,
	425	+ dtype=float)
	426	+ X_cl = pd.concat([sub_clust[x_cols], cl_dummies], axis=1)
	427	+ res = _safe_ols(sub_clust[y_col], X_cl)
	428	+ results["Spatial clusters"] = _result_to_dict(res)
	429	+ except Exception as e:
	430	+ logger.error("Spatial clusters OLS failed: %s", e)
	431	+
	432	+ # 5. Spatial lag
	433	+ try:
	434	+ from sklearn.neighbors import BallTree
	435	+ sub_lag = sub.dropna(subset=coord_cols).copy()
	436	+ tree = BallTree(np.radians(sub_lag[coord_cols].values),
	437	+ metric="haversine")
	438	+ # 5 nearest neighbours, radius in radians (~2 km)
	439	+ dists, indices = tree.query(
	440	+ np.radians(sub_lag[coord_cols].values), k=6)
	441	+ # Exclude self (index 0), compute mean price of neighbours
	442	+ neighbour_prices = []
	443	+ price_arr = sub_lag[y_col].values
	444	+ for row_idx in range(len(indices)):
	445	+ nbr_idx = indices[row_idx, 1:] # skip self
	446	+ neighbour_prices.append(price_arr[nbr_idx].mean())
	447	+ sub_lag["spatial_lag_price"] = neighbour_prices
	448	+ X_lag = pd.concat(
	449	+ [sub_lag[x_cols],
	450	+ sub_lag[["spatial_lag_price"]]], axis=1)
	451	+ res = _safe_ols(sub_lag[y_col], X_lag)
	452	+ results["Spatial lag"] = _result_to_dict(res)
	453	+ except Exception as e:
	454	+ logger.error("Spatial lag OLS failed: %s", e)
	455	+
	456	+ logger.info("Location robustness: %d specs estimated.", len(results))
	457	+ return results
	458	+
	459	+
	460	+# ---------------------------------------------------------------------------
	461	+# 5. Host type
	462	+# ---------------------------------------------------------------------------
	463	+
	464	+def robustness_host_type(
	465	+ df: pd.DataFrame,
	466	+ y_col: str,
	467	+ x_cols: List[str],
	468	+ superhost_col: str = "is_superhost",
	469	+ listing_count_col: Optional[str] = None,
	470	+ min_obs: int = 50,
	471	+) -> Dict[str, Dict[str, Any]]:
	472	+ """
	473	+ Compare regressions for different host types.
	474	+
	475	+ Specifications:
	476	+ 1. All hosts
	477	+ 2. Single-listing hosts (if listing count available)
	478	+ 3. Multi-listing hosts
	479	+ 4. Superhosts only
	480	+ 5. Non-superhosts
	481	+
	482	+ Parameters
	483	+ ----------
	484	+ df : pd.DataFrame
	485	+ y_col : str
	486	+ x_cols : list[str]
	487	+ superhost_col : str
	488	+ listing_count_col : str, optional
	489	+ Column with number of listings per host.
	490	+ min_obs : int
	491	+
	492	+ Returns
	493	+ -------
	494	+ dict
	495	+ """
	496	+ logger.info("Robustness: host type stratification.")
	497	+ sub = df.dropna(subset=[y_col] + x_cols).copy()
	498	+ results: Dict[str, Dict[str, Any]] = {}
	499	+
	500	+ # Remove host columns from x_cols to avoid multicollinearity
	501	+ host_keywords = {"host", "superhost", "listing_count"}
	502	+ x_clean = [c for c in x_cols
	503	+ if not any(kw in c.lower() for kw in host_keywords)]
	504	+
	505	+ def _run_subsample(name: str, mask: pd.Series) -> None:
	506	+ grp = sub.loc[mask]
	507	+ if len(grp) < min_obs:
	508	+ logger.warning("Skipping %s (n=%d).", name, len(grp))
	509	+ return
	510	+ x_valid = [c for c in x_clean if grp[c].nunique() > 1]
	511	+ if not x_valid:
	512	+ return
	513	+ try:
	514	+ res = _safe_ols(grp[y_col], grp[x_valid])
	515	+ results[name] = _result_to_dict(res)
	516	+ except Exception as e:
	517	+ logger.error("%s failed: %s", name, e)
	518	+
	519	+ # 1. All hosts
	520	+ _run_subsample("All hosts", pd.Series(True, index=sub.index))
	521	+
	522	+ # 2/3. Single vs multi listing
	523	+ if listing_count_col and listing_count_col in sub.columns:
	524	+ _run_subsample(
	525	+ "Single-listing hosts",
	526	+ sub[listing_count_col] == 1,
	527	+ )
	528	+ _run_subsample(
	529	+ "Multi-listing hosts",
	530	+ sub[listing_count_col] > 1,
	531	+ )
	532	+
	533	+ # 4/5. Superhosts
	534	+ if superhost_col in sub.columns:
	535	+ # Handle bool/string/numeric
	536	+ sh = sub[superhost_col]
	537	+ if sh.dtype == bool:
	538	+ is_super = sh
	539	+ elif sh.dtype == object:
	540	+ is_super = sh.str.lower().isin(["true", "t", "1", "yes"])
	541	+ else:
	542	+ is_super = sh.astype(bool)
	543	+
	544	+ _run_subsample("Superhosts", is_super)
	545	+ _run_subsample("Non-superhosts", ~is_super)
	546	+
	547	+ logger.info("Host-type robustness: %d sub-samples estimated.", len(results))
	548	+ return results
	549	+
	550	+
	551	+# ---------------------------------------------------------------------------
	552	+# Master runner
	553	+# ---------------------------------------------------------------------------
	554	+
	555	+def run_all_robustness(
	556	+ df: pd.DataFrame,
	557	+ y_col: str,
	558	+ x_cols: List[str],
	559	+ room_col: str = "property_type",
	560	+ geo_col: str = "city",
	561	+ superhost_col: str = "is_superhost",
	562	+ listing_count_col: Optional[str] = None,
	563	+) -> Dict[str, Dict[str, Dict[str, Any]]]:
	564	+ """
	565	+ Run all robustness checks and return a nested summary dict.
	566	+
	567	+ Parameters
	568	+ ----------
	569	+ df : pd.DataFrame
	570	+ y_col : str
	571	+ x_cols : list[str]
	572	+ room_col, geo_col, superhost_col, listing_count_col : str
	573	+
	574	+ Returns
	575	+ -------
	576	+ dict
	577	+ ``{check_category: {spec_name: results_dict}}``
	578	+ """
	579	+ logger.info("Running full robustness battery.")
	580	+ all_results: Dict[str, Dict[str, Dict[str, Any]]] = {}
	581	+
	582	+ all_results["Winsorization"] = robustness_winsorization(
	583	+ df, y_col, x_cols)
	584	+
	585	+ all_results["Room Type"] = robustness_by_room_type(
	586	+ df, y_col, x_cols, room_col=room_col)
	587	+
	588	+ all_results["Price Segment"] = robustness_by_price_segment(
	589	+ df, y_col, x_cols)
	590	+
	591	+ all_results["Location Controls"] = robustness_location_controls(
	592	+ df, y_col, x_cols, geo_col=geo_col)
	593	+
	594	+ all_results["Host Type"] = robustness_host_type(
	595	+ df, y_col, x_cols, superhost_col=superhost_col,
	596	+ listing_count_col=listing_count_col)
	597	+
	598	+ total_specs = sum(len(v) for v in all_results.values())
	599	+ logger.info("Full robustness battery complete: %d total specifications "
	600	+ "across %d categories.", total_specs, len(all_results))
	601	+ return all_results
	602	+
	603	+
	604	+# ---------------------------------------------------------------------------
	605	+# Summary formatter
	606	+# ---------------------------------------------------------------------------
	607	+
	608	+def format_robustness_summary(
	609	+ results_dict: Dict[str, Dict[str, Dict[str, Any]]],
	610	+ key_variables: Optional[List[str]] = None,
	611	+) -> pd.DataFrame:
	612	+ """
	613	+ Create a compact comparison table from all robustness results.
	614	+
	615	+ Parameters
	616	+ ----------
	617	+ results_dict : dict
	618	+ Output of ``run_all_robustness()``.
	619	+ key_variables : list[str], optional
	620	+ Subset of variables to display coefficients for.
	621	+ If None, only model-level statistics are shown.
	622	+
	623	+ Returns
	624	+ -------
	625	+ pd.DataFrame
	626	+ Summary table with one row per specification.
	627	+ """
	628	+ logger.info("Formatting robustness summary.")
	629	+ records: List[Dict[str, Any]] = []
	630	+
	631	+ for category, specs in results_dict.items():
	632	+ for spec_name, spec_data in specs.items():
	633	+ row: Dict[str, Any] = {
	634	+ "Category": category,
	635	+ "Specification": spec_name,
	636	+ "N": spec_data.get("N", ""),
	637	+ "R2": spec_data.get("R2", ""),
	638	+ "Adj_R2": spec_data.get("Adj_R2", ""),
	639	+ "AIC": spec_data.get("AIC", ""),
	640	+ }
	641	+
	642	+ # Count significant coefficients
	643	+ var_entries = {
	644	+ k: v for k, v in spec_data.items()
	645	+ if isinstance(v, dict) and "coef" in v
	646	+ }
	647	+ n_total = len(var_entries)
	648	+ n_sig = sum(
	649	+ 1 for v in var_entries.values()
	650	+ if v.get("pvalue", 1.0) < 0.05
	651	+ )
	652	+ row["Vars (total)"] = n_total
	653	+ row["Vars (p<.05)"] = n_sig
	654	+
	655	+ # Key variable coefficients
	656	+ if key_variables:
	657	+ for kv in key_variables:
	658	+ if kv in spec_data and isinstance(spec_data[kv], dict):
	659	+ c = spec_data[kv]
	660	+ pval = c.get("pvalue", 1.0)
	661	+ stars = ""
	662	+ if pval <= 0.001:
	663	+ stars = "***"
	664	+ elif pval <= 0.01:
	665	+ stars = "**"
	666	+ elif pval <= 0.05:
	667	+ stars = "*"
	668	+ row[f"b({kv})"] = f"{c['coef']:.4f}{stars}"
	669	+ else:
	670	+ row[f"b({kv})"] = ""
	671	+
	672	+ records.append(row)
	673	+
	674	+ summary = pd.DataFrame(records)
	675	+ return summary

added src/shap_analysis.py +168 −0

		`@@ -0,0 +1,785 @@`
	1	+"""
	2	+shap_analysis.py
	3	+================
	4	+SHAP-based interpretability analysis for hedonic pricing models.
	5	+
	6	+Provides SHAP value computation, publication-quality summary / dependence /
	7	+interaction / waterfall plots, partial-dependence plots, accumulated local
	8	+effects (ALE), and hedonic premium interpretation. Gracefully degrades to
	9	+permutation importance and manual PDP when the ``shap`` library is not
	10	+installed.
	11	+
	12	+Author: Simon-Pierre Boucher
	13	+Date: 2026-05-20
	14	+"""
	15	+
	16	+from __future__ import annotations
	17	+
	18	+import logging
	19	+import os
	20	+import warnings
	21	+from pathlib import Path
	22	+from typing import Any, Dict, List, Optional, Tuple, Union
	23	+
	24	+import matplotlib
	25	+import matplotlib.pyplot as plt
	26	+import numpy as np
	27	+import pandas as pd
	28	+from sklearn.inspection import PartialDependenceDisplay, partial_dependence
	29	+from sklearn.metrics import r2_score
	30	+
	31	+logger = logging.getLogger(__name__)
	32	+
	33	+# Publication-quality defaults ------------------------------------------------
	34	+matplotlib.rcParams.update(
	35	+ {
	36	+ "figure.dpi": 150,
	37	+ "savefig.dpi": 300,
	38	+ "font.family": "serif",
	39	+ "font.size": 11,
	40	+ "axes.titlesize": 13,
	41	+ "axes.labelsize": 12,
	42	+ "xtick.labelsize": 10,
	43	+ "ytick.labelsize": 10,
	44	+ "legend.fontsize": 10,
	45	+ "figure.figsize": (10, 7),
	46	+ "savefig.bbox": "tight",
	47	+ "savefig.pad_inches": 0.1,
	48	+ }
	49	+)
	50	+
	51	+# Try importing shap ----------------------------------------------------------
	52	+_SHAP_AVAILABLE = False
	53	+try:
	54	+ import shap
	55	+
	56	+ _SHAP_AVAILABLE = True
	57	+except ImportError:
	58	+ logger.info(
	59	+ "shap library not installed. Falling back to permutation importance "
	60	+ "and manual PDP implementations."
	61	+ )
	62	+
	63	+
	64	+# ---------------------------------------------------------------------------
	65	+# Utility helpers
	66	+# ---------------------------------------------------------------------------
	67	+
	68	+
	69	+def _ensure_dir(path: str) -> None:
	70	+ """Create parent directories for path if they do not exist."""
	71	+ Path(path).parent.mkdir(parents=True, exist_ok=True)
	72	+
	73	+
	74	+def _save_figure(fig: plt.Figure, save_path: str) -> None:
	75	+ """Save a matplotlib figure in both PNG and PDF."""
	76	+ _ensure_dir(save_path)
	77	+ base, _ = os.path.splitext(save_path)
	78	+ fig.savefig(f"{base}.png", format="png")
	79	+ fig.savefig(f"{base}.pdf", format="pdf")
	80	+ logger.info("Figure saved: %s.png / .pdf", base)
	81	+ plt.close(fig)
	82	+
	83	+
	84	+def _to_dataframe(X: np.ndarray, feature_names: List[str]) -> pd.DataFrame:
	85	+ """Convert an ndarray to a DataFrame with proper column names."""
	86	+ return pd.DataFrame(X, columns=feature_names)
	87	+
	88	+
	89	+# ---------------------------------------------------------------------------
	90	+# Permutation-importance fallback (when shap is unavailable)
	91	+# ---------------------------------------------------------------------------
	92	+
	93	+
	94	+def _permutation_importance(
	95	+ model: Any,
	96	+ X: np.ndarray,
	97	+ y: Optional[np.ndarray] = None,
	98	+ n_repeats: int = 10,
	99	+ random_state: int = 42,
	100	+) -> np.ndarray:
	101	+ """Compute permutation importance as a (n_features,) array of mean
	102	+ absolute importance values.
	103	+
	104	+ When y is not provided, variance of prediction changes is used instead.
	105	+ """
	106	+ rng = np.random.RandomState(random_state)
	107	+ baseline = model.predict(X)
	108	+
	109	+ if y is not None:
	110	+ baseline_score = r2_score(y, baseline)
	111	+ else:
	112	+ baseline_score = None
	113	+
	114	+ importances = np.zeros(X.shape[1])
	115	+ for j in range(X.shape[1]):
	116	+ scores: List[float] = []
	117	+ for _ in range(n_repeats):
	118	+ X_perm = X.copy()
	119	+ X_perm[:, j] = rng.permutation(X_perm[:, j])
	120	+ preds_perm = model.predict(X_perm)
	121	+ if y is not None and baseline_score is not None:
	122	+ scores.append(baseline_score - r2_score(y, preds_perm))
	123	+ else:
	124	+ scores.append(float(np.mean(np.abs(baseline - preds_perm))))
	125	+ importances[j] = float(np.mean(scores))
	126	+ return importances
	127	+
	128	+
	129	+# ---------------------------------------------------------------------------
	130	+# SHAP value computation
	131	+# ---------------------------------------------------------------------------
	132	+
	133	+
	134	+def compute_shap_values(
	135	+ model: Any,
	136	+ X: np.ndarray,
	137	+ feature_names: List[str],
	138	+ max_samples: int = 500,
	139	+) -> Union["shap.Explanation", np.ndarray]:
	140	+ """Compute SHAP values for the given model and data.
	141	+
	142	+ Strategy:
	143	+ 1. If ``shap`` is available, use ``TreeExplainer`` for tree-based models;
	144	+ otherwise fall back to ``KernelExplainer`` (on a subsample for speed).
	145	+ 2. If ``shap`` is not installed, return a permutation-importance
	146	+ matrix shaped ``(n_samples, n_features)`` as a rough substitute.
	147	+
	148	+ Parameters
	149	+ ----------
	150	+ model : estimator
	151	+ Fitted sklearn-compatible model.
	152	+ X : np.ndarray
	153	+ Feature matrix.
	154	+ feature_names : list[str]
	155	+ Column names.
	156	+ max_samples : int
	157	+ Maximum number of background samples for ``KernelExplainer``.
	158	+
	159	+ Returns
	160	+ -------
	161	+ shap.Explanation \| np.ndarray
	162	+ SHAP values object or permutation-based importance matrix.
	163	+ """
	164	+ if _SHAP_AVAILABLE:
	165	+ model_type = type(model).__name__
	166	+ tree_types = {
	167	+ "RandomForestRegressor",
	168	+ "GradientBoostingRegressor",

Diff truncated — file too large.

		`@@ -0,0 +1,10 @@`
	1	+# Raw data — Inside Airbnb, Quebec
	2	+
	3	+\| Item \| Value \|
	4	+\|---\|---\|
	5	+\| Source \| Inside Airbnb public extracts (listings.csv) for Quebec markets, cleaned sample \|
	6	+\| File expected here \| `cleaned_dataset_sample.csv` — 5,000 listings × 19 variables \|
	7	+\| Retained after cleaning \| 3,412 listings with a valid nightly price (CAD 10 – 25,000), 1,906 in the full specification with reviews and location \|
	8	+\| Fields \| nightly price, room/property type, bedrooms, bathrooms, guests, amenities list, host attributes, review counts and scores, latitude/longitude, neighbourhood, listing title/description \|
	9	+\| Extraction \| May 2026 \|
	10	+\| Access \| Inside Airbnb data are released under CC BY 4.0; the cleaned sample is not committed here — download the Quebec `listings.csv` files from insideairbnb.com and run `make download` after placing the file in this folder. \|

		`@@ -0,0 +1,8 @@`
	1	+,log_price,bedrooms,bathrooms,guests_count,amenities_count,rating,num_reviews
	2	+log_price,1.000,,,,,,
	3	+bedrooms,0.111***,1.000,,,,,
	4	+bathrooms,0.138*,0.704*,1.000,,,,
	5	+guests_count,0.072*,0.806,0.637**,1.000,,,
	6	+amenities_count,-0.051,0.322,0.290,0.370*,1.000,,
	7	+rating,0.006,0.129*,0.157,0.113,0.274*,1.000,
	8	+num_reviews,-0.236*,-0.061,-0.049,-0.029,0.095,0.072**,1.000