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使用 1996 年至 2024 年的房源数据构建波兰的享乐房价指数.pdf

上传人: Fl****zo 编号:718599 2025-06-22 17页 1.48MB

1、Radosaw TrojanekConstructing Hedonic House Price and Rent Indices for Poland using listings data from 1996-2024The National Science Centre of Poland partly supported this work under Grant number 2021/43/B/HS4/01213.IntroductionTransactions:-most reliable indicator of a propertys market valueProblems

2、 with transaction data from PPR(Property Price Register)in Poland:-the time lag between the completion of transaction(notarial deed)and the date of entry into the property price register(e.g.Warsaw up to 6 months)-the transaction price can be set several months before the transaction date(in case of

3、 new-builts for Warsaw average gap of 530 days between price agreement and transaction date)-insufficient property description for effective application of hedonic pricing models(e.g.no information on technology construction,quality of real estate)-difficult to judge if transaction was on market con

4、ditions-PPR does not include cooperative ownership property right which holds share of 20-25%of secondary houisng market(existing builts)IntroductionListings:-Available online,detail description of property Problems:property listings may contain inaccurate information regarding virtually every aspec

5、t of the real estate(location,area,construction year,)date problem(date of scrapping,date of modification,date of listing first time which one to use?)the same property may be listed on multiple portals(sometimes with different list prices).a property may stay on a portal website for many weeks or m

6、onths,with a list price that may change over time.there is no guarantee that the property will sell,particularly if the stated list price exceeds current market conditionsIntroductionThe subject scope:Dwellings prices on the secondary housing market,involve both property rights and cooperative prope

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本文主要研究了如何利用1996至2024年的房源数据为波兰构建享乐型房价和租金指数。以下是关键点: 1. 研究背景:波兰财产价格登记存在时滞、信息不足等问题,影响市场价值评估。 2. 数据来源:采用在线房源列表,覆盖28个城市房价和9个城市租金。 3. 数据量:分析超过4百万条房源数据,其中1996-2008年数据来源于档案广告。 4. 数据处理:剔除无地址、价格、面积信息的数据,去重,剔除异常值,并补充区域信息。 5. 研究方法:采用享乐定价模型,构建房价和租金指数。 6. 数据优势:房源数据能及时反映住房市场变化,弥补官方数据的时滞问题。 7. 核心数据:2019年波兰二级市场52%的交易发生在研究覆盖的城市;70%的长期租赁房源位于这些城市。 综上,尽管房源数据存在一定问题,但经过适当处理,仍可有效地构建住房市场的价格和租金指数。
"波兰房价租金如何走?" "在线房源信息靠谱吗?" "如何紧跟波兰房市动态?"
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