这一篇内容是2026年第一个月的新房成交数据。众所周知，春节前后楼市肯定都不咋的，销量可以说是一年当中的最低谷。这里也给大家打个 预防针。 那究竟节前的1月份楼市到底跌了多少？下面还是看兔博士的月报吧。 数据说明Explain：数据出自兔博士app，2026年1月新房成交数据，环比2025年12月。 南京NANJING 1500 25000 20000 0 2月 9月 10月 11月 12月 1月 3月 4月 5月 6月 7月 8月 1月 成交量连续4个月上涨结束 | 分总价段 $ 200万以下 $ 200-300万 7% 700套 +18% 11% 13890元 +2% 39% 成交量占比 $ 300-500万 24% 429套 -47% 32719元 -5% 19% $ 800万以上 200万以下 ● 200-300万 300-500万 ● 500-800万 116套 -72% 800万以上 44148元 -13% 333套 -57% 22516元 -16% $ 500-800万 197套 -73% 41428元 -8% 200万以下成交占主导,200-300万成交价下跌16% 区域名字 六合 70套 - ...

大家都在猜，楼市到底要搞什么样的救援措施才行？是大规模撒钱普惠式的，还是找准问题点精准施策？ 这700亿的房贷贴息，是不是能给楼市加把劲，还是说纯粹花了冤枉钱，效果打折扣呢？ 到了2026年开年的时候，楼市里出现了一些微妙的变化，商品房的待售面积已经连续九个月在下降，二手房的成交量快要超过新房了，这个市场正站在一个 重要的转折关口上。 700亿能干点啥呢？也许连几座超级工厂都建不起，也不够修条跨省高铁，但据清华大学的研究显示，这笔钱能撬动高达280万亿的居民房地产资产，真是意 想不到的规模呀。 就像用一颗石子拨动整个海面，起了一圈圈涟漪，听着好像天方夜谭，但实际上却直击当前楼市的根本难题。 这个时候推出贴息政策，是不是正合适呢？效果会不会带来预期的改善？ 精准投喂才有效 这几年楼市啊，走得磕磕绊绊的，真是再贴切不过了。 这段时间，成交一直不太理想，绝大部分城市的新房库存都堆得挺厉害。到2025年11月底，全国商品房的待售面积还剩7.5306亿平方米，虽然连续九个月在 下降，但住宅的待售面积还高达3.9361亿平方米，去库存的压力还是挺大的。 存量房的业主们更是挺没底的，房价不涨反而跌，想卖又卖不掉，想换房却买 ...

Core Insights - The real estate market is showing signs of stabilization, particularly in first-tier cities, with a notable decrease in price decline rates and increased transaction volumes [5][9] - There is a clear divergence in the real estate market, with first-tier cities recovering while second and third-tier cities continue to struggle [10][9] Group 1: Market Trends - Recent data indicates that new home prices in first-tier cities have only decreased by 0.2% month-on-month, a significant improvement compared to previous months [5] - The transaction volume in areas outside the fifth ring road in Beijing has surged by 35% in viewings and doubled in sales within 20 days of policy relaxation [5][4] - Predictions suggest that transaction volumes in areas outside the fifth ring road may increase by 25%-40% from August to September, contributing to a "small spring" in the real estate market [4] Group 2: Market Divergence - There is a stark contrast between the performance of first-tier cities and that of second and third-tier cities, with first-tier cities showing signs of recovery while the latter remain under pressure [10][9] - Inventory pressure is significantly different, with 70% of the national housing inventory concentrated in second and third-tier cities, leading to longer absorption periods compared to first-tier core areas [9] Group 3: Consumer Sentiment - Buyers are increasingly making decisions based on personal circumstances rather than market speculation, with some opting to purchase homes for stability rather than investment [11][16] - The sentiment among buyers reflects a shift in perspective, viewing homes more as consumer goods rather than investment assets, emphasizing the importance of location and livability [18]

Core Viewpoint - The article discusses the discrepancy between the predicted decline in housing prices and the actual market behavior, highlighting that many developers are not lowering prices as expected despite reports of a significant drop in average housing prices across various cities [1][3]. Group 1: Market Analysis - Reports indicate that the national average housing price has decreased from 11,000 yuan per square meter to 9,500 yuan per square meter, a decline of over 15% [1]. - Major cities like Zhengzhou, Tianjin, Shijiazhuang, Jinan, Wuhan, and Taiyuan have seen housing prices revert to levels from three to five years ago [1]. Group 2: Sales Strategies - Sales personnel in real estate often quote higher prices to create a perception of demand, leveraging the "buy high, not low" psychology of buyers, which discourages them from reducing prices [3]. - Developers are reluctant to lower prices due to potential backlash from existing homeowners who may demand compensation or refunds, impacting the sales office's operations [3]. Group 3: Pricing Discrepancies - The prices advertised by sales offices are typically higher than the actual transaction prices, as buyers usually negotiate lower final prices [5]. - Media and authoritative data often reflect actual transaction prices rather than initial quotes, leading to a discrepancy in perceived market conditions [5]. Group 4: Data Interpretation - The data available online primarily covers both new and second-hand housing prices, with second-hand prices providing a more accurate reflection of market fluctuations [5]. - New housing prices are influenced by policies such as price limits, making them less representative of the overall real estate market conditions [5].